{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"adult_train.csv\")\n",
    "test_data = pd.read_csv(\"adult_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Martial_Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>245487</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>176756</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>28887</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>292175</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>193524</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>302146</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>76845</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>117037</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>109015</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>216851</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>168294</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>193366</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33811</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>204461</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>337992</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>179137</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>325033</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>160216</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>345898</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>139180</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>15020</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287372</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>45</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>252208</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202822</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129912</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>119199</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>199655</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>111499</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>198216</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>260761</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>99359</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>255835</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>27242</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>34066</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>84661</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>116138</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>321865</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>310152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age          Workclass  fnlwgt      Education  Education_Num  \\\n",
       "0       39          State-gov   77516      Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311      Bachelors             13   \n",
       "2       38            Private  215646        HS-grad              9   \n",
       "3       53            Private  234721           11th              7   \n",
       "4       28            Private  338409      Bachelors             13   \n",
       "5       37            Private  284582        Masters             14   \n",
       "6       49            Private  160187            9th              5   \n",
       "7       52   Self-emp-not-inc  209642        HS-grad              9   \n",
       "8       31            Private   45781        Masters             14   \n",
       "9       42            Private  159449      Bachelors             13   \n",
       "10      37            Private  280464   Some-college             10   \n",
       "11      30          State-gov  141297      Bachelors             13   \n",
       "12      23            Private  122272      Bachelors             13   \n",
       "13      32            Private  205019     Assoc-acdm             12   \n",
       "14      40            Private  121772      Assoc-voc             11   \n",
       "15      34            Private  245487        7th-8th              4   \n",
       "16      25   Self-emp-not-inc  176756        HS-grad              9   \n",
       "17      32            Private  186824        HS-grad              9   \n",
       "18      38            Private   28887           11th              7   \n",
       "19      43   Self-emp-not-inc  292175        Masters             14   \n",
       "20      40            Private  193524      Doctorate             16   \n",
       "21      54            Private  302146        HS-grad              9   \n",
       "22      35        Federal-gov   76845            9th              5   \n",
       "23      43            Private  117037           11th              7   \n",
       "24      59            Private  109015        HS-grad              9   \n",
       "25      56          Local-gov  216851      Bachelors             13   \n",
       "26      19            Private  168294        HS-grad              9   \n",
       "27      54                NaN  180211   Some-college             10   \n",
       "28      39            Private  367260        HS-grad              9   \n",
       "29      49            Private  193366        HS-grad              9   \n",
       "...    ...                ...     ...            ...            ...   \n",
       "32531   30                NaN   33811      Bachelors             13   \n",
       "32532   34            Private  204461      Doctorate             16   \n",
       "32533   54            Private  337992      Bachelors             13   \n",
       "32534   37            Private  179137   Some-college             10   \n",
       "32535   22            Private  325033           12th              8   \n",
       "32536   34            Private  160216      Bachelors             13   \n",
       "32537   30            Private  345898        HS-grad              9   \n",
       "32538   38            Private  139180      Bachelors             13   \n",
       "32539   71                NaN  287372      Doctorate             16   \n",
       "32540   45          State-gov  252208        HS-grad              9   \n",
       "32541   41                NaN  202822        HS-grad              9   \n",
       "32542   72                NaN  129912        HS-grad              9   \n",
       "32543   45          Local-gov  119199     Assoc-acdm             12   \n",
       "32544   31            Private  199655        Masters             14   \n",
       "32545   39          Local-gov  111499     Assoc-acdm             12   \n",
       "32546   37            Private  198216     Assoc-acdm             12   \n",
       "32547   43            Private  260761        HS-grad              9   \n",
       "32548   65   Self-emp-not-inc   99359    Prof-school             15   \n",
       "32549   43          State-gov  255835   Some-college             10   \n",
       "32550   43   Self-emp-not-inc   27242   Some-college             10   \n",
       "32551   32            Private   34066           10th              6   \n",
       "32552   43            Private   84661      Assoc-voc             11   \n",
       "32553   32            Private  116138        Masters             14   \n",
       "32554   53            Private  321865        Masters             14   \n",
       "32555   22            Private  310152   Some-college             10   \n",
       "32556   27            Private  257302     Assoc-acdm             12   \n",
       "32557   40            Private  154374        HS-grad              9   \n",
       "32558   58            Private  151910        HS-grad              9   \n",
       "32559   22            Private  201490        HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927        HS-grad              9   \n",
       "\n",
       "               Martial_Status          Occupation     Relationship  \\\n",
       "0               Never-married        Adm-clerical    Not-in-family   \n",
       "1          Married-civ-spouse     Exec-managerial          Husband   \n",
       "2                    Divorced   Handlers-cleaners    Not-in-family   \n",
       "3          Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "4          Married-civ-spouse      Prof-specialty             Wife   \n",
       "5          Married-civ-spouse     Exec-managerial             Wife   \n",
       "6       Married-spouse-absent       Other-service    Not-in-family   \n",
       "7          Married-civ-spouse     Exec-managerial          Husband   \n",
       "8               Never-married      Prof-specialty    Not-in-family   \n",
       "9          Married-civ-spouse     Exec-managerial          Husband   \n",
       "10         Married-civ-spouse     Exec-managerial          Husband   \n",
       "11         Married-civ-spouse      Prof-specialty          Husband   \n",
       "12              Never-married        Adm-clerical        Own-child   \n",
       "13              Never-married               Sales    Not-in-family   \n",
       "14         Married-civ-spouse        Craft-repair          Husband   \n",
       "15         Married-civ-spouse    Transport-moving          Husband   \n",
       "16              Never-married     Farming-fishing        Own-child   \n",
       "17              Never-married   Machine-op-inspct        Unmarried   \n",
       "18         Married-civ-spouse               Sales          Husband   \n",
       "19                   Divorced     Exec-managerial        Unmarried   \n",
       "20         Married-civ-spouse      Prof-specialty          Husband   \n",
       "21                  Separated       Other-service        Unmarried   \n",
       "22         Married-civ-spouse     Farming-fishing          Husband   \n",
       "23         Married-civ-spouse    Transport-moving          Husband   \n",
       "24                   Divorced        Tech-support        Unmarried   \n",
       "25         Married-civ-spouse        Tech-support          Husband   \n",
       "26              Never-married        Craft-repair        Own-child   \n",
       "27         Married-civ-spouse                 NaN          Husband   \n",
       "28                   Divorced     Exec-managerial    Not-in-family   \n",
       "29         Married-civ-spouse        Craft-repair          Husband   \n",
       "...                       ...                 ...              ...   \n",
       "32531           Never-married                 NaN    Not-in-family   \n",
       "32532      Married-civ-spouse      Prof-specialty          Husband   \n",
       "32533      Married-civ-spouse     Exec-managerial          Husband   \n",
       "32534                Divorced        Adm-clerical        Unmarried   \n",
       "32535           Never-married     Protective-serv        Own-child   \n",
       "32536           Never-married     Exec-managerial    Not-in-family   \n",
       "32537           Never-married        Craft-repair    Not-in-family   \n",
       "32538                Divorced      Prof-specialty        Unmarried   \n",
       "32539      Married-civ-spouse                 NaN          Husband   \n",
       "32540               Separated        Adm-clerical        Own-child   \n",
       "32541               Separated                 NaN    Not-in-family   \n",
       "32542      Married-civ-spouse                 NaN          Husband   \n",
       "32543                Divorced      Prof-specialty        Unmarried   \n",
       "32544                Divorced       Other-service    Not-in-family   \n",
       "32545      Married-civ-spouse        Adm-clerical             Wife   \n",
       "32546                Divorced        Tech-support    Not-in-family   \n",
       "32547      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32548           Never-married      Prof-specialty    Not-in-family   \n",
       "32549                Divorced        Adm-clerical   Other-relative   \n",
       "32550      Married-civ-spouse        Craft-repair          Husband   \n",
       "32551      Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "32552      Married-civ-spouse               Sales          Husband   \n",
       "32553           Never-married        Tech-support    Not-in-family   \n",
       "32554      Married-civ-spouse     Exec-managerial          Husband   \n",
       "32555           Never-married     Protective-serv    Not-in-family   \n",
       "32556      Married-civ-spouse        Tech-support             Wife   \n",
       "32557      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32558                 Widowed        Adm-clerical        Unmarried   \n",
       "32559           Never-married        Adm-clerical        Own-child   \n",
       "32560      Married-civ-spouse     Exec-managerial             Wife   \n",
       "\n",
       "                      Race      Sex  Capital_Gain  Capital_Loss  \\\n",
       "0                    White     Male          2174             0   \n",
       "1                    White     Male             0             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    Black   Female             0             0   \n",
       "5                    White   Female             0             0   \n",
       "6                    Black   Female             0             0   \n",
       "7                    White     Male             0             0   \n",
       "8                    White   Female         14084             0   \n",
       "9                    White     Male          5178             0   \n",
       "10                   Black     Male             0             0   \n",
       "11      Asian-Pac-Islander     Male             0             0   \n",
       "12                   White   Female             0             0   \n",
       "13                   Black     Male             0             0   \n",
       "14      Asian-Pac-Islander     Male             0             0   \n",
       "15      Amer-Indian-Eskimo     Male             0             0   \n",
       "16                   White     Male             0             0   \n",
       "17                   White     Male             0             0   \n",
       "18                   White     Male             0             0   \n",
       "19                   White   Female             0             0   \n",
       "20                   White     Male             0             0   \n",
       "21                   Black   Female             0             0   \n",
       "22                   Black     Male             0             0   \n",
       "23                   White     Male             0          2042   \n",
       "24                   White   Female             0             0   \n",
       "25                   White     Male             0             0   \n",
       "26                   White     Male             0             0   \n",
       "27      Asian-Pac-Islander     Male             0             0   \n",
       "28                   White     Male             0             0   \n",
       "29                   White     Male             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "32531   Asian-Pac-Islander   Female             0             0   \n",
       "32532                White     Male             0             0   \n",
       "32533   Asian-Pac-Islander     Male             0             0   \n",
       "32534                White   Female             0             0   \n",
       "32535                Black     Male             0             0   \n",
       "32536                White   Female             0             0   \n",
       "32537                Black     Male             0             0   \n",
       "32538                Black   Female         15020             0   \n",
       "32539                White     Male             0             0   \n",
       "32540                White   Female             0             0   \n",
       "32541                Black   Female             0             0   \n",
       "32542                White     Male             0             0   \n",
       "32543                White   Female             0             0   \n",
       "32544                Other   Female             0             0   \n",
       "32545                White   Female             0             0   \n",
       "32546                White   Female             0             0   \n",
       "32547                White     Male             0             0   \n",
       "32548                White     Male          1086             0   \n",
       "32549                White   Female             0             0   \n",
       "32550                White     Male             0             0   \n",
       "32551   Amer-Indian-Eskimo     Male             0             0   \n",
       "32552                White     Male             0             0   \n",
       "32553   Asian-Pac-Islander     Male             0             0   \n",
       "32554                White     Male             0             0   \n",
       "32555                White     Male             0             0   \n",
       "32556                White   Female             0             0   \n",
       "32557                White     Male             0             0   \n",
       "32558                White   Female             0             0   \n",
       "32559                White     Male             0             0   \n",
       "32560                White   Female         15024             0   \n",
       "\n",
       "       Hours_per_week        Country  Target  \n",
       "0                  40  United-States       0  \n",
       "1                  13  United-States       0  \n",
       "2                  40  United-States       0  \n",
       "3                  40  United-States       0  \n",
       "4                  40           Cuba       0  \n",
       "5                  40  United-States       0  \n",
       "6                  16        Jamaica       0  \n",
       "7                  45  United-States       1  \n",
       "8                  50  United-States       1  \n",
       "9                  40  United-States       1  \n",
       "10                 80  United-States       1  \n",
       "11                 40          India       1  \n",
       "12                 30  United-States       0  \n",
       "13                 50  United-States       0  \n",
       "14                 40            NaN       1  \n",
       "15                 45         Mexico       0  \n",
       "16                 35  United-States       0  \n",
       "17                 40  United-States       0  \n",
       "18                 50  United-States       0  \n",
       "19                 45  United-States       1  \n",
       "20                 60  United-States       1  \n",
       "21                 20  United-States       0  \n",
       "22                 40  United-States       0  \n",
       "23                 40  United-States       0  \n",
       "24                 40  United-States       0  \n",
       "25                 40  United-States       1  \n",
       "26                 40  United-States       0  \n",
       "27                 60          South       1  \n",
       "28                 80  United-States       0  \n",
       "29                 40  United-States       0  \n",
       "...               ...            ...     ...  \n",
       "32531              99  United-States       0  \n",
       "32532              60  United-States       1  \n",
       "32533              50          Japan       1  \n",
       "32534              39  United-States       0  \n",
       "32535              35  United-States       0  \n",
       "32536              55  United-States       1  \n",
       "32537              46  United-States       0  \n",
       "32538              45  United-States       1  \n",
       "32539              10  United-States       1  \n",
       "32540              40  United-States       0  \n",
       "32541              32  United-States       0  \n",
       "32542              25  United-States       0  \n",
       "32543              48  United-States       0  \n",
       "32544              30  United-States       0  \n",
       "32545              20  United-States       1  \n",
       "32546              40  United-States       0  \n",
       "32547              40         Mexico       0  \n",
       "32548              60  United-States       0  \n",
       "32549              40  United-States       0  \n",
       "32550              50  United-States       0  \n",
       "32551              40  United-States       0  \n",
       "32552              45  United-States       0  \n",
       "32553              11         Taiwan       0  \n",
       "32554              40  United-States       1  \n",
       "32555              40  United-States       0  \n",
       "32556              38  United-States       0  \n",
       "32557              40  United-States       1  \n",
       "32558              40  United-States       0  \n",
       "32559              20  United-States       0  \n",
       "32560              40  United-States       1  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Martial_Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227026</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>104626</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>369667</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>104996</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "      <td>Private</td>\n",
       "      <td>184454</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>6418</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>212465</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>Private</td>\n",
       "      <td>82091</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299831</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>279724</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>346189</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>444554</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>128354</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>60548</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>85019</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>107914</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>238588</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132015</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>220931</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>Peru</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>205947</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>432824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>236427</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>134446</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>99516</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>109282</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26711</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2936</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>Private</td>\n",
       "      <td>117909</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>229647</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>149347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>23157</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>93977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>159691</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>176967</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>344436</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>430340</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>202168</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>82720</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>269623</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>136405</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>139347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>224655</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>247547</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>292710</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>173449</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>285570</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>Private</td>\n",
       "      <td>89686</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>440129</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>350977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>349230</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>245211</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16281 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age          Workclass  fnlwgt      Education  Education_Num  \\\n",
       "0       25            Private  226802           11th              7   \n",
       "1       38            Private   89814        HS-grad              9   \n",
       "2       28          Local-gov  336951     Assoc-acdm             12   \n",
       "3       44            Private  160323   Some-college             10   \n",
       "4       18                NaN  103497   Some-college             10   \n",
       "5       34            Private  198693           10th              6   \n",
       "6       29                NaN  227026        HS-grad              9   \n",
       "7       63   Self-emp-not-inc  104626    Prof-school             15   \n",
       "8       24            Private  369667   Some-college             10   \n",
       "9       55            Private  104996        7th-8th              4   \n",
       "10      65            Private  184454        HS-grad              9   \n",
       "11      36        Federal-gov  212465      Bachelors             13   \n",
       "12      26            Private   82091        HS-grad              9   \n",
       "13      58                NaN  299831        HS-grad              9   \n",
       "14      48            Private  279724        HS-grad              9   \n",
       "15      43            Private  346189        Masters             14   \n",
       "16      20          State-gov  444554   Some-college             10   \n",
       "17      43            Private  128354        HS-grad              9   \n",
       "18      37            Private   60548        HS-grad              9   \n",
       "19      40            Private   85019      Doctorate             16   \n",
       "20      34            Private  107914      Bachelors             13   \n",
       "21      34            Private  238588   Some-college             10   \n",
       "22      72                NaN  132015        7th-8th              4   \n",
       "23      25            Private  220931      Bachelors             13   \n",
       "24      25            Private  205947      Bachelors             13   \n",
       "25      45   Self-emp-not-inc  432824        HS-grad              9   \n",
       "26      22            Private  236427        HS-grad              9   \n",
       "27      23            Private  134446        HS-grad              9   \n",
       "28      54            Private   99516        HS-grad              9   \n",
       "29      32   Self-emp-not-inc  109282   Some-college             10   \n",
       "...    ...                ...     ...            ...            ...   \n",
       "16251   81                NaN   26711      Assoc-voc             11   \n",
       "16252   60            Private  117909      Assoc-voc             11   \n",
       "16253   39            Private  229647      Bachelors             13   \n",
       "16254   38            Private  149347        Masters             14   \n",
       "16255   43          Local-gov   23157        Masters             14   \n",
       "16256   23            Private   93977        HS-grad              9   \n",
       "16257   73       Self-emp-inc  159691   Some-college             10   \n",
       "16258   35            Private  176967   Some-college             10   \n",
       "16259   66            Private  344436        HS-grad              9   \n",
       "16260   27            Private  430340   Some-college             10   \n",
       "16261   40            Private  202168    Prof-school             15   \n",
       "16262   51            Private   82720        HS-grad              9   \n",
       "16263   22            Private  269623   Some-college             10   \n",
       "16264   64   Self-emp-not-inc  136405        HS-grad              9   \n",
       "16265   50          Local-gov  139347        Masters             14   \n",
       "16266   55            Private  224655        HS-grad              9   \n",
       "16267   38            Private  247547      Assoc-voc             11   \n",
       "16268   58            Private  292710     Assoc-acdm             12   \n",
       "16269   32            Private  173449        HS-grad              9   \n",
       "16270   48            Private  285570        HS-grad              9   \n",
       "16271   61            Private   89686        HS-grad              9   \n",
       "16272   31            Private  440129        HS-grad              9   \n",
       "16273   25            Private  350977        HS-grad              9   \n",
       "16274   48          Local-gov  349230        Masters             14   \n",
       "16275   33            Private  245211      Bachelors             13   \n",
       "16276   39            Private  215419      Bachelors             13   \n",
       "16277   64                NaN  321403        HS-grad              9   \n",
       "16278   38            Private  374983      Bachelors             13   \n",
       "16279   44            Private   83891      Bachelors             13   \n",
       "16280   35       Self-emp-inc  182148      Bachelors             13   \n",
       "\n",
       "            Martial_Status          Occupation     Relationship  \\\n",
       "0            Never-married   Machine-op-inspct        Own-child   \n",
       "1       Married-civ-spouse     Farming-fishing          Husband   \n",
       "2       Married-civ-spouse     Protective-serv          Husband   \n",
       "3       Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "4            Never-married                 NaN        Own-child   \n",
       "5            Never-married       Other-service    Not-in-family   \n",
       "6            Never-married                 NaN        Unmarried   \n",
       "7       Married-civ-spouse      Prof-specialty          Husband   \n",
       "8            Never-married       Other-service        Unmarried   \n",
       "9       Married-civ-spouse        Craft-repair          Husband   \n",
       "10      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "11      Married-civ-spouse        Adm-clerical          Husband   \n",
       "12           Never-married        Adm-clerical    Not-in-family   \n",
       "13      Married-civ-spouse                 NaN          Husband   \n",
       "14      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "15      Married-civ-spouse     Exec-managerial          Husband   \n",
       "16           Never-married       Other-service        Own-child   \n",
       "17      Married-civ-spouse        Adm-clerical             Wife   \n",
       "18                 Widowed   Machine-op-inspct        Unmarried   \n",
       "19      Married-civ-spouse      Prof-specialty          Husband   \n",
       "20      Married-civ-spouse        Tech-support          Husband   \n",
       "21           Never-married       Other-service        Own-child   \n",
       "22                Divorced                 NaN    Not-in-family   \n",
       "23           Never-married      Prof-specialty    Not-in-family   \n",
       "24      Married-civ-spouse      Prof-specialty          Husband   \n",
       "25      Married-civ-spouse        Craft-repair          Husband   \n",
       "26           Never-married        Adm-clerical        Own-child   \n",
       "27               Separated   Machine-op-inspct        Unmarried   \n",
       "28      Married-civ-spouse        Craft-repair          Husband   \n",
       "29           Never-married      Prof-specialty    Not-in-family   \n",
       "...                    ...                 ...              ...   \n",
       "16251   Married-civ-spouse                 NaN          Husband   \n",
       "16252   Married-civ-spouse      Prof-specialty          Husband   \n",
       "16253        Never-married        Tech-support    Not-in-family   \n",
       "16254   Married-civ-spouse      Prof-specialty          Husband   \n",
       "16255   Married-civ-spouse     Exec-managerial          Husband   \n",
       "16256        Never-married   Machine-op-inspct        Own-child   \n",
       "16257             Divorced     Exec-managerial    Not-in-family   \n",
       "16258   Married-civ-spouse     Protective-serv          Husband   \n",
       "16259              Widowed               Sales   Other-relative   \n",
       "16260        Never-married               Sales    Not-in-family   \n",
       "16261   Married-civ-spouse      Prof-specialty          Husband   \n",
       "16262   Married-civ-spouse        Craft-repair          Husband   \n",
       "16263        Never-married        Craft-repair        Own-child   \n",
       "16264              Widowed     Farming-fishing    Not-in-family   \n",
       "16265   Married-civ-spouse      Prof-specialty             Wife   \n",
       "16266            Separated     Priv-house-serv    Not-in-family   \n",
       "16267        Never-married        Adm-clerical        Unmarried   \n",
       "16268             Divorced      Prof-specialty    Not-in-family   \n",
       "16269   Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "16270   Married-civ-spouse        Adm-clerical          Husband   \n",
       "16271   Married-civ-spouse               Sales          Husband   \n",
       "16272   Married-civ-spouse        Craft-repair          Husband   \n",
       "16273        Never-married       Other-service        Own-child   \n",
       "16274             Divorced       Other-service    Not-in-family   \n",
       "16275        Never-married      Prof-specialty        Own-child   \n",
       "16276             Divorced      Prof-specialty    Not-in-family   \n",
       "16277              Widowed                 NaN   Other-relative   \n",
       "16278   Married-civ-spouse      Prof-specialty          Husband   \n",
       "16279             Divorced        Adm-clerical        Own-child   \n",
       "16280   Married-civ-spouse     Exec-managerial          Husband   \n",
       "\n",
       "                      Race      Sex  Capital_Gain  Capital_Loss  \\\n",
       "0                    Black     Male             0             0   \n",
       "1                    White     Male             0             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male          7688             0   \n",
       "4                    White   Female             0             0   \n",
       "5                    White     Male             0             0   \n",
       "6                    Black     Male             0             0   \n",
       "7                    White     Male          3103             0   \n",
       "8                    White   Female             0             0   \n",
       "9                    White     Male             0             0   \n",
       "10                   White     Male          6418             0   \n",
       "11                   White     Male             0             0   \n",
       "12                   White   Female             0             0   \n",
       "13                   White     Male             0             0   \n",
       "14                   White     Male          3103             0   \n",
       "15                   White     Male             0             0   \n",
       "16                   White     Male             0             0   \n",
       "17                   White   Female             0             0   \n",
       "18                   White   Female             0             0   \n",
       "19      Asian-Pac-Islander     Male             0             0   \n",
       "20                   White     Male             0             0   \n",
       "21                   Black   Female             0             0   \n",
       "22                   White   Female             0             0   \n",
       "23                   White     Male             0             0   \n",
       "24                   White     Male             0             0   \n",
       "25                   White     Male          7298             0   \n",
       "26                   White     Male             0             0   \n",
       "27                   Black     Male             0             0   \n",
       "28                   White     Male             0             0   \n",
       "29                   White     Male             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "16251                White     Male          2936             0   \n",
       "16252                White     Male          7688             0   \n",
       "16253                White   Female             0          1669   \n",
       "16254                White     Male             0             0   \n",
       "16255                White     Male             0          1902   \n",
       "16256                White     Male             0             0   \n",
       "16257                White   Female             0             0   \n",
       "16258                White     Male             0             0   \n",
       "16259                White   Female             0             0   \n",
       "16260                White   Female             0             0   \n",
       "16261                White     Male         15024             0   \n",
       "16262                White     Male             0             0   \n",
       "16263                White     Male             0             0   \n",
       "16264                White     Male             0             0   \n",
       "16265                White   Female             0             0   \n",
       "16266                White   Female             0             0   \n",
       "16267                Black   Female             0             0   \n",
       "16268                White     Male             0             0   \n",
       "16269                White     Male             0             0   \n",
       "16270                White     Male             0             0   \n",
       "16271                White     Male             0             0   \n",
       "16272                White     Male             0             0   \n",
       "16273                White   Female             0             0   \n",
       "16274                White     Male             0             0   \n",
       "16275                White     Male             0             0   \n",
       "16276                White   Female             0             0   \n",
       "16277                Black     Male             0             0   \n",
       "16278                White     Male             0             0   \n",
       "16279   Asian-Pac-Islander     Male          5455             0   \n",
       "16280                White     Male             0             0   \n",
       "\n",
       "       Hours_per_week        Country  Target  \n",
       "0                  40  United-States       0  \n",
       "1                  50  United-States       0  \n",
       "2                  40  United-States       1  \n",
       "3                  40  United-States       1  \n",
       "4                  30  United-States       0  \n",
       "5                  30  United-States       0  \n",
       "6                  40  United-States       0  \n",
       "7                  32  United-States       1  \n",
       "8                  40  United-States       0  \n",
       "9                  10  United-States       0  \n",
       "10                 40  United-States       1  \n",
       "11                 40  United-States       0  \n",
       "12                 39  United-States       0  \n",
       "13                 35  United-States       0  \n",
       "14                 48  United-States       1  \n",
       "15                 50  United-States       1  \n",
       "16                 25  United-States       0  \n",
       "17                 30  United-States       0  \n",
       "18                 20  United-States       0  \n",
       "19                 45            NaN       1  \n",
       "20                 47  United-States       1  \n",
       "21                 35  United-States       0  \n",
       "22                  6  United-States       0  \n",
       "23                 43           Peru       0  \n",
       "24                 40  United-States       0  \n",
       "25                 90  United-States       1  \n",
       "26                 20  United-States       0  \n",
       "27                 54  United-States       0  \n",
       "28                 35  United-States       0  \n",
       "29                 60  United-States       0  \n",
       "...               ...            ...     ...  \n",
       "16251              20  United-States       0  \n",
       "16252              40  United-States       1  \n",
       "16253              40  United-States       0  \n",
       "16254              50  United-States       1  \n",
       "16255              50  United-States       1  \n",
       "16256              40  United-States       0  \n",
       "16257              40  United-States       0  \n",
       "16258              40  United-States       0  \n",
       "16259               8  United-States       0  \n",
       "16260              45  United-States       0  \n",
       "16261              55  United-States       1  \n",
       "16262              40  United-States       0  \n",
       "16263              40  United-States       0  \n",
       "16264              32  United-States       0  \n",
       "16265              40            NaN       1  \n",
       "16266              32  United-States       0  \n",
       "16267              40  United-States       0  \n",
       "16268              36  United-States       0  \n",
       "16269              40  United-States       0  \n",
       "16270              40  United-States       0  \n",
       "16271              48  United-States       0  \n",
       "16272              40  United-States       0  \n",
       "16273              40  United-States       0  \n",
       "16274              40  United-States       0  \n",
       "16275              40  United-States       0  \n",
       "16276              36  United-States       0  \n",
       "16277              40  United-States       0  \n",
       "16278              50  United-States       0  \n",
       "16279              40  United-States       0  \n",
       "16280              60  United-States       1  \n",
       "\n",
       "[16281 rows x 15 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Martial_Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>245487</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>176756</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>28887</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>292175</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>193524</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>302146</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>76845</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>117037</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>109015</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>216851</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>168294</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>193366</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26711</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2936</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>Private</td>\n",
       "      <td>117909</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>229647</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>149347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>23157</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>93977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>159691</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>176967</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>344436</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>430340</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>202168</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>82720</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>269623</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>136405</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>139347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>224655</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>247547</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>292710</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>173449</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>285570</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>Private</td>\n",
       "      <td>89686</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>440129</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>350977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>349230</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>245211</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age          Workclass  fnlwgt      Education  Education_Num  \\\n",
       "0       39          State-gov   77516      Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311      Bachelors             13   \n",
       "2       38            Private  215646        HS-grad              9   \n",
       "3       53            Private  234721           11th              7   \n",
       "4       28            Private  338409      Bachelors             13   \n",
       "5       37            Private  284582        Masters             14   \n",
       "6       49            Private  160187            9th              5   \n",
       "7       52   Self-emp-not-inc  209642        HS-grad              9   \n",
       "8       31            Private   45781        Masters             14   \n",
       "9       42            Private  159449      Bachelors             13   \n",
       "10      37            Private  280464   Some-college             10   \n",
       "11      30          State-gov  141297      Bachelors             13   \n",
       "12      23            Private  122272      Bachelors             13   \n",
       "13      32            Private  205019     Assoc-acdm             12   \n",
       "14      40            Private  121772      Assoc-voc             11   \n",
       "15      34            Private  245487        7th-8th              4   \n",
       "16      25   Self-emp-not-inc  176756        HS-grad              9   \n",
       "17      32            Private  186824        HS-grad              9   \n",
       "18      38            Private   28887           11th              7   \n",
       "19      43   Self-emp-not-inc  292175        Masters             14   \n",
       "20      40            Private  193524      Doctorate             16   \n",
       "21      54            Private  302146        HS-grad              9   \n",
       "22      35        Federal-gov   76845            9th              5   \n",
       "23      43            Private  117037           11th              7   \n",
       "24      59            Private  109015        HS-grad              9   \n",
       "25      56          Local-gov  216851      Bachelors             13   \n",
       "26      19            Private  168294        HS-grad              9   \n",
       "27      54                NaN  180211   Some-college             10   \n",
       "28      39            Private  367260        HS-grad              9   \n",
       "29      49            Private  193366        HS-grad              9   \n",
       "...    ...                ...     ...            ...            ...   \n",
       "16251   81                NaN   26711      Assoc-voc             11   \n",
       "16252   60            Private  117909      Assoc-voc             11   \n",
       "16253   39            Private  229647      Bachelors             13   \n",
       "16254   38            Private  149347        Masters             14   \n",
       "16255   43          Local-gov   23157        Masters             14   \n",
       "16256   23            Private   93977        HS-grad              9   \n",
       "16257   73       Self-emp-inc  159691   Some-college             10   \n",
       "16258   35            Private  176967   Some-college             10   \n",
       "16259   66            Private  344436        HS-grad              9   \n",
       "16260   27            Private  430340   Some-college             10   \n",
       "16261   40            Private  202168    Prof-school             15   \n",
       "16262   51            Private   82720        HS-grad              9   \n",
       "16263   22            Private  269623   Some-college             10   \n",
       "16264   64   Self-emp-not-inc  136405        HS-grad              9   \n",
       "16265   50          Local-gov  139347        Masters             14   \n",
       "16266   55            Private  224655        HS-grad              9   \n",
       "16267   38            Private  247547      Assoc-voc             11   \n",
       "16268   58            Private  292710     Assoc-acdm             12   \n",
       "16269   32            Private  173449        HS-grad              9   \n",
       "16270   48            Private  285570        HS-grad              9   \n",
       "16271   61            Private   89686        HS-grad              9   \n",
       "16272   31            Private  440129        HS-grad              9   \n",
       "16273   25            Private  350977        HS-grad              9   \n",
       "16274   48          Local-gov  349230        Masters             14   \n",
       "16275   33            Private  245211      Bachelors             13   \n",
       "16276   39            Private  215419      Bachelors             13   \n",
       "16277   64                NaN  321403        HS-grad              9   \n",
       "16278   38            Private  374983      Bachelors             13   \n",
       "16279   44            Private   83891      Bachelors             13   \n",
       "16280   35       Self-emp-inc  182148      Bachelors             13   \n",
       "\n",
       "               Martial_Status          Occupation     Relationship  \\\n",
       "0               Never-married        Adm-clerical    Not-in-family   \n",
       "1          Married-civ-spouse     Exec-managerial          Husband   \n",
       "2                    Divorced   Handlers-cleaners    Not-in-family   \n",
       "3          Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "4          Married-civ-spouse      Prof-specialty             Wife   \n",
       "5          Married-civ-spouse     Exec-managerial             Wife   \n",
       "6       Married-spouse-absent       Other-service    Not-in-family   \n",
       "7          Married-civ-spouse     Exec-managerial          Husband   \n",
       "8               Never-married      Prof-specialty    Not-in-family   \n",
       "9          Married-civ-spouse     Exec-managerial          Husband   \n",
       "10         Married-civ-spouse     Exec-managerial          Husband   \n",
       "11         Married-civ-spouse      Prof-specialty          Husband   \n",
       "12              Never-married        Adm-clerical        Own-child   \n",
       "13              Never-married               Sales    Not-in-family   \n",
       "14         Married-civ-spouse        Craft-repair          Husband   \n",
       "15         Married-civ-spouse    Transport-moving          Husband   \n",
       "16              Never-married     Farming-fishing        Own-child   \n",
       "17              Never-married   Machine-op-inspct        Unmarried   \n",
       "18         Married-civ-spouse               Sales          Husband   \n",
       "19                   Divorced     Exec-managerial        Unmarried   \n",
       "20         Married-civ-spouse      Prof-specialty          Husband   \n",
       "21                  Separated       Other-service        Unmarried   \n",
       "22         Married-civ-spouse     Farming-fishing          Husband   \n",
       "23         Married-civ-spouse    Transport-moving          Husband   \n",
       "24                   Divorced        Tech-support        Unmarried   \n",
       "25         Married-civ-spouse        Tech-support          Husband   \n",
       "26              Never-married        Craft-repair        Own-child   \n",
       "27         Married-civ-spouse                 NaN          Husband   \n",
       "28                   Divorced     Exec-managerial    Not-in-family   \n",
       "29         Married-civ-spouse        Craft-repair          Husband   \n",
       "...                       ...                 ...              ...   \n",
       "16251      Married-civ-spouse                 NaN          Husband   \n",
       "16252      Married-civ-spouse      Prof-specialty          Husband   \n",
       "16253           Never-married        Tech-support    Not-in-family   \n",
       "16254      Married-civ-spouse      Prof-specialty          Husband   \n",
       "16255      Married-civ-spouse     Exec-managerial          Husband   \n",
       "16256           Never-married   Machine-op-inspct        Own-child   \n",
       "16257                Divorced     Exec-managerial    Not-in-family   \n",
       "16258      Married-civ-spouse     Protective-serv          Husband   \n",
       "16259                 Widowed               Sales   Other-relative   \n",
       "16260           Never-married               Sales    Not-in-family   \n",
       "16261      Married-civ-spouse      Prof-specialty          Husband   \n",
       "16262      Married-civ-spouse        Craft-repair          Husband   \n",
       "16263           Never-married        Craft-repair        Own-child   \n",
       "16264                 Widowed     Farming-fishing    Not-in-family   \n",
       "16265      Married-civ-spouse      Prof-specialty             Wife   \n",
       "16266               Separated     Priv-house-serv    Not-in-family   \n",
       "16267           Never-married        Adm-clerical        Unmarried   \n",
       "16268                Divorced      Prof-specialty    Not-in-family   \n",
       "16269      Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "16270      Married-civ-spouse        Adm-clerical          Husband   \n",
       "16271      Married-civ-spouse               Sales          Husband   \n",
       "16272      Married-civ-spouse        Craft-repair          Husband   \n",
       "16273           Never-married       Other-service        Own-child   \n",
       "16274                Divorced       Other-service    Not-in-family   \n",
       "16275           Never-married      Prof-specialty        Own-child   \n",
       "16276                Divorced      Prof-specialty    Not-in-family   \n",
       "16277                 Widowed                 NaN   Other-relative   \n",
       "16278      Married-civ-spouse      Prof-specialty          Husband   \n",
       "16279                Divorced        Adm-clerical        Own-child   \n",
       "16280      Married-civ-spouse     Exec-managerial          Husband   \n",
       "\n",
       "                      Race      Sex  Capital_Gain  Capital_Loss  \\\n",
       "0                    White     Male          2174             0   \n",
       "1                    White     Male             0             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    Black   Female             0             0   \n",
       "5                    White   Female             0             0   \n",
       "6                    Black   Female             0             0   \n",
       "7                    White     Male             0             0   \n",
       "8                    White   Female         14084             0   \n",
       "9                    White     Male          5178             0   \n",
       "10                   Black     Male             0             0   \n",
       "11      Asian-Pac-Islander     Male             0             0   \n",
       "12                   White   Female             0             0   \n",
       "13                   Black     Male             0             0   \n",
       "14      Asian-Pac-Islander     Male             0             0   \n",
       "15      Amer-Indian-Eskimo     Male             0             0   \n",
       "16                   White     Male             0             0   \n",
       "17                   White     Male             0             0   \n",
       "18                   White     Male             0             0   \n",
       "19                   White   Female             0             0   \n",
       "20                   White     Male             0             0   \n",
       "21                   Black   Female             0             0   \n",
       "22                   Black     Male             0             0   \n",
       "23                   White     Male             0          2042   \n",
       "24                   White   Female             0             0   \n",
       "25                   White     Male             0             0   \n",
       "26                   White     Male             0             0   \n",
       "27      Asian-Pac-Islander     Male             0             0   \n",
       "28                   White     Male             0             0   \n",
       "29                   White     Male             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "16251                White     Male          2936             0   \n",
       "16252                White     Male          7688             0   \n",
       "16253                White   Female             0          1669   \n",
       "16254                White     Male             0             0   \n",
       "16255                White     Male             0          1902   \n",
       "16256                White     Male             0             0   \n",
       "16257                White   Female             0             0   \n",
       "16258                White     Male             0             0   \n",
       "16259                White   Female             0             0   \n",
       "16260                White   Female             0             0   \n",
       "16261                White     Male         15024             0   \n",
       "16262                White     Male             0             0   \n",
       "16263                White     Male             0             0   \n",
       "16264                White     Male             0             0   \n",
       "16265                White   Female             0             0   \n",
       "16266                White   Female             0             0   \n",
       "16267                Black   Female             0             0   \n",
       "16268                White     Male             0             0   \n",
       "16269                White     Male             0             0   \n",
       "16270                White     Male             0             0   \n",
       "16271                White     Male             0             0   \n",
       "16272                White     Male             0             0   \n",
       "16273                White   Female             0             0   \n",
       "16274                White     Male             0             0   \n",
       "16275                White     Male             0             0   \n",
       "16276                White   Female             0             0   \n",
       "16277                Black     Male             0             0   \n",
       "16278                White     Male             0             0   \n",
       "16279   Asian-Pac-Islander     Male          5455             0   \n",
       "16280                White     Male             0             0   \n",
       "\n",
       "       Hours_per_week        Country  Target  \n",
       "0                  40  United-States       0  \n",
       "1                  13  United-States       0  \n",
       "2                  40  United-States       0  \n",
       "3                  40  United-States       0  \n",
       "4                  40           Cuba       0  \n",
       "5                  40  United-States       0  \n",
       "6                  16        Jamaica       0  \n",
       "7                  45  United-States       1  \n",
       "8                  50  United-States       1  \n",
       "9                  40  United-States       1  \n",
       "10                 80  United-States       1  \n",
       "11                 40          India       1  \n",
       "12                 30  United-States       0  \n",
       "13                 50  United-States       0  \n",
       "14                 40            NaN       1  \n",
       "15                 45         Mexico       0  \n",
       "16                 35  United-States       0  \n",
       "17                 40  United-States       0  \n",
       "18                 50  United-States       0  \n",
       "19                 45  United-States       1  \n",
       "20                 60  United-States       1  \n",
       "21                 20  United-States       0  \n",
       "22                 40  United-States       0  \n",
       "23                 40  United-States       0  \n",
       "24                 40  United-States       0  \n",
       "25                 40  United-States       1  \n",
       "26                 40  United-States       0  \n",
       "27                 60          South       1  \n",
       "28                 80  United-States       0  \n",
       "29                 40  United-States       0  \n",
       "...               ...            ...     ...  \n",
       "16251              20  United-States       0  \n",
       "16252              40  United-States       1  \n",
       "16253              40  United-States       0  \n",
       "16254              50  United-States       1  \n",
       "16255              50  United-States       1  \n",
       "16256              40  United-States       0  \n",
       "16257              40  United-States       0  \n",
       "16258              40  United-States       0  \n",
       "16259               8  United-States       0  \n",
       "16260              45  United-States       0  \n",
       "16261              55  United-States       1  \n",
       "16262              40  United-States       0  \n",
       "16263              40  United-States       0  \n",
       "16264              32  United-States       0  \n",
       "16265              40            NaN       1  \n",
       "16266              32  United-States       0  \n",
       "16267              40  United-States       0  \n",
       "16268              36  United-States       0  \n",
       "16269              40  United-States       0  \n",
       "16270              40  United-States       0  \n",
       "16271              48  United-States       0  \n",
       "16272              40  United-States       0  \n",
       "16273              40  United-States       0  \n",
       "16274              40  United-States       0  \n",
       "16275              40  United-States       0  \n",
       "16276              36  United-States       0  \n",
       "16277              40  United-States       0  \n",
       "16278              50  United-States       0  \n",
       "16279              40  United-States       0  \n",
       "16280              60  United-States       1  \n",
       "\n",
       "[48842 rows x 15 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将训练集和测试集的数据合并 一起做数据预处理\n",
    "all_data = pd.concat((train_data,test_data),axis=0)\n",
    "all_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               False\n",
       "Workclass          True\n",
       "fnlwgt            False\n",
       "Education         False\n",
       "Education_Num     False\n",
       "Martial_Status    False\n",
       "Occupation         True\n",
       "Relationship      False\n",
       "Race              False\n",
       "Sex               False\n",
       "Capital_Gain      False\n",
       "Capital_Loss      False\n",
       "Hours_per_week    False\n",
       "Country            True\n",
       "Target            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#填充空值，下一语句用于检测出来哪一列存在空值\n",
    "all_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Workclass Occupation Country中存在空值\n",
    "all_data[\"Workclass\"] = all_data[\"Workclass\"].fillna(\"Private\")\n",
    "all_data[\"Occupation\"] = all_data[\"Occupation\"].fillna(\"Prof-specialty\")\n",
    "all_data[\"Country\"] = all_data[\"Country\"].fillna(\"United-States\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203488    21\n",
       "120277    19\n",
       "190290    19\n",
       "125892    18\n",
       "126569    18\n",
       "99185     17\n",
       "113364    17\n",
       "126675    17\n",
       "186934    16\n",
       "111567    16\n",
       "120131    15\n",
       "117963    15\n",
       "127651    15\n",
       "123011    15\n",
       "121124    14\n",
       "108140    14\n",
       "193882    14\n",
       "148995    14\n",
       "188246    14\n",
       "111483    14\n",
       "164190    14\n",
       "123983    14\n",
       "194630    14\n",
       "132879    14\n",
       "136986    14\n",
       "174789    13\n",
       "214542    13\n",
       "117789    13\n",
       "144778    13\n",
       "156464    13\n",
       "          ..\n",
       "196947     1\n",
       "145748     1\n",
       "336215     1\n",
       "259131     1\n",
       "213339     1\n",
       "158046     1\n",
       "170336     1\n",
       "233825     1\n",
       "198863     1\n",
       "172037     1\n",
       "127277     1\n",
       "391468     1\n",
       "233729     1\n",
       "112900     1\n",
       "45317      1\n",
       "41223      1\n",
       "250121     1\n",
       "114955     1\n",
       "194828     1\n",
       "123151     1\n",
       "334096     1\n",
       "69905      1\n",
       "209173     1\n",
       "141590     1\n",
       "284952     1\n",
       "170272     1\n",
       "137444     1\n",
       "172327     1\n",
       "111368     1\n",
       "208174     1\n",
       "Name: fnlwgt, Length: 28523, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[\"fnlwgt\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.drop('fnlwgt',axis=1,inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Martial_Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          Workclass   Education  Education_Num       Martial_Status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           Occupation    Relationship    Race      Sex  Capital_Gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   Capital_Loss  Hours_per_week        Country  Target  \n",
       "0             0              40  United-States       0  \n",
       "1             0              13  United-States       0  \n",
       "2             0              40  United-States       0  \n",
       "3             0              40  United-States       0  \n",
       "4             0              40           Cuba       0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看Education_Num与Target的关系  一般受教育程度越高，工资越高\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Education_Num[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Education_Num[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Education_Num')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看Martial_Status婚姻状况与Target的关系 \n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Martial_Status[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Martial_Status[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Martial_Status')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看Workclass工作性质与Target的关系  一般受教育程度越高，工资越高\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Workclass[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Workclass[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Workclass')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看职业Occupation与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Occupation[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Occupation[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Occupation')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看种族Race与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Race[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Race[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Race')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看性别Sex与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Sex[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Sex[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Sex')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看关系Relationship与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Relationship[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Relationship[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Relationship')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看国家Country与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Country[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Country[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Country')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看受教育程度Education与Target的关系\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Target_than50K = all_data.Education[all_data.Target==1].value_counts()\n",
    "Target_less50K = all_data.Education[all_data.Target==0].value_counts()\n",
    "df = pd.DataFrame({u'Target_than50K':Target_than50K,u'Target_less50K':Target_less50K})\n",
    "df.plot(kind='bar',stacked=True)\n",
    "plt.xlabel(u'Education')\n",
    "plt.ylabel(u'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Martial_Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2936</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age          Workclass  Education_Num          Martial_Status  \\\n",
       "0       39          State-gov             13           Never-married   \n",
       "1       50   Self-emp-not-inc             13      Married-civ-spouse   \n",
       "2       38            Private              9                Divorced   \n",
       "3       53            Private              7      Married-civ-spouse   \n",
       "4       28            Private             13      Married-civ-spouse   \n",
       "5       37            Private             14      Married-civ-spouse   \n",
       "6       49            Private              5   Married-spouse-absent   \n",
       "7       52   Self-emp-not-inc              9      Married-civ-spouse   \n",
       "8       31            Private             14           Never-married   \n",
       "9       42            Private             13      Married-civ-spouse   \n",
       "10      37            Private             10      Married-civ-spouse   \n",
       "11      30          State-gov             13      Married-civ-spouse   \n",
       "12      23            Private             13           Never-married   \n",
       "13      32            Private             12           Never-married   \n",
       "14      40            Private             11      Married-civ-spouse   \n",
       "15      34            Private              4      Married-civ-spouse   \n",
       "16      25   Self-emp-not-inc              9           Never-married   \n",
       "17      32            Private              9           Never-married   \n",
       "18      38            Private              7      Married-civ-spouse   \n",
       "19      43   Self-emp-not-inc             14                Divorced   \n",
       "20      40            Private             16      Married-civ-spouse   \n",
       "21      54            Private              9               Separated   \n",
       "22      35        Federal-gov              5      Married-civ-spouse   \n",
       "23      43            Private              7      Married-civ-spouse   \n",
       "24      59            Private              9                Divorced   \n",
       "25      56          Local-gov             13      Married-civ-spouse   \n",
       "26      19            Private              9           Never-married   \n",
       "27      54            Private             10      Married-civ-spouse   \n",
       "28      39            Private              9                Divorced   \n",
       "29      49            Private              9      Married-civ-spouse   \n",
       "...    ...                ...            ...                     ...   \n",
       "16251   81            Private             11      Married-civ-spouse   \n",
       "16252   60            Private             11      Married-civ-spouse   \n",
       "16253   39            Private             13           Never-married   \n",
       "16254   38            Private             14      Married-civ-spouse   \n",
       "16255   43          Local-gov             14      Married-civ-spouse   \n",
       "16256   23            Private              9           Never-married   \n",
       "16257   73       Self-emp-inc             10                Divorced   \n",
       "16258   35            Private             10      Married-civ-spouse   \n",
       "16259   66            Private              9                 Widowed   \n",
       "16260   27            Private             10           Never-married   \n",
       "16261   40            Private             15      Married-civ-spouse   \n",
       "16262   51            Private              9      Married-civ-spouse   \n",
       "16263   22            Private             10           Never-married   \n",
       "16264   64   Self-emp-not-inc              9                 Widowed   \n",
       "16265   50          Local-gov             14      Married-civ-spouse   \n",
       "16266   55            Private              9               Separated   \n",
       "16267   38            Private             11           Never-married   \n",
       "16268   58            Private             12                Divorced   \n",
       "16269   32            Private              9      Married-civ-spouse   \n",
       "16270   48            Private              9      Married-civ-spouse   \n",
       "16271   61            Private              9      Married-civ-spouse   \n",
       "16272   31            Private              9      Married-civ-spouse   \n",
       "16273   25            Private              9           Never-married   \n",
       "16274   48          Local-gov             14                Divorced   \n",
       "16275   33            Private             13           Never-married   \n",
       "16276   39            Private             13                Divorced   \n",
       "16277   64            Private              9                 Widowed   \n",
       "16278   38            Private             13      Married-civ-spouse   \n",
       "16279   44            Private             13                Divorced   \n",
       "16280   35       Self-emp-inc             13      Married-civ-spouse   \n",
       "\n",
       "               Occupation     Relationship                 Race      Sex  \\\n",
       "0            Adm-clerical    Not-in-family                White     Male   \n",
       "1         Exec-managerial          Husband                White     Male   \n",
       "2       Handlers-cleaners    Not-in-family                White     Male   \n",
       "3       Handlers-cleaners          Husband                Black     Male   \n",
       "4          Prof-specialty             Wife                Black   Female   \n",
       "5         Exec-managerial             Wife                White   Female   \n",
       "6           Other-service    Not-in-family                Black   Female   \n",
       "7         Exec-managerial          Husband                White     Male   \n",
       "8          Prof-specialty    Not-in-family                White   Female   \n",
       "9         Exec-managerial          Husband                White     Male   \n",
       "10        Exec-managerial          Husband                Black     Male   \n",
       "11         Prof-specialty          Husband   Asian-Pac-Islander     Male   \n",
       "12           Adm-clerical        Own-child                White   Female   \n",
       "13                  Sales    Not-in-family                Black     Male   \n",
       "14           Craft-repair          Husband   Asian-Pac-Islander     Male   \n",
       "15       Transport-moving          Husband   Amer-Indian-Eskimo     Male   \n",
       "16        Farming-fishing        Own-child                White     Male   \n",
       "17      Machine-op-inspct        Unmarried                White     Male   \n",
       "18                  Sales          Husband                White     Male   \n",
       "19        Exec-managerial        Unmarried                White   Female   \n",
       "20         Prof-specialty          Husband                White     Male   \n",
       "21          Other-service        Unmarried                Black   Female   \n",
       "22        Farming-fishing          Husband                Black     Male   \n",
       "23       Transport-moving          Husband                White     Male   \n",
       "24           Tech-support        Unmarried                White   Female   \n",
       "25           Tech-support          Husband                White     Male   \n",
       "26           Craft-repair        Own-child                White     Male   \n",
       "27         Prof-specialty          Husband   Asian-Pac-Islander     Male   \n",
       "28        Exec-managerial    Not-in-family                White     Male   \n",
       "29           Craft-repair          Husband                White     Male   \n",
       "...                   ...              ...                  ...      ...   \n",
       "16251      Prof-specialty          Husband                White     Male   \n",
       "16252      Prof-specialty          Husband                White     Male   \n",
       "16253        Tech-support    Not-in-family                White   Female   \n",
       "16254      Prof-specialty          Husband                White     Male   \n",
       "16255     Exec-managerial          Husband                White     Male   \n",
       "16256   Machine-op-inspct        Own-child                White     Male   \n",
       "16257     Exec-managerial    Not-in-family                White   Female   \n",
       "16258     Protective-serv          Husband                White     Male   \n",
       "16259               Sales   Other-relative                White   Female   \n",
       "16260               Sales    Not-in-family                White   Female   \n",
       "16261      Prof-specialty          Husband                White     Male   \n",
       "16262        Craft-repair          Husband                White     Male   \n",
       "16263        Craft-repair        Own-child                White     Male   \n",
       "16264     Farming-fishing    Not-in-family                White     Male   \n",
       "16265      Prof-specialty             Wife                White   Female   \n",
       "16266     Priv-house-serv    Not-in-family                White   Female   \n",
       "16267        Adm-clerical        Unmarried                Black   Female   \n",
       "16268      Prof-specialty    Not-in-family                White     Male   \n",
       "16269   Handlers-cleaners          Husband                White     Male   \n",
       "16270        Adm-clerical          Husband                White     Male   \n",
       "16271               Sales          Husband                White     Male   \n",
       "16272        Craft-repair          Husband                White     Male   \n",
       "16273       Other-service        Own-child                White   Female   \n",
       "16274       Other-service    Not-in-family                White     Male   \n",
       "16275      Prof-specialty        Own-child                White     Male   \n",
       "16276      Prof-specialty    Not-in-family                White   Female   \n",
       "16277      Prof-specialty   Other-relative                Black     Male   \n",
       "16278      Prof-specialty          Husband                White     Male   \n",
       "16279        Adm-clerical        Own-child   Asian-Pac-Islander     Male   \n",
       "16280     Exec-managerial          Husband                White     Male   \n",
       "\n",
       "       Capital_Gain  Capital_Loss  Hours_per_week        Country  Target  \n",
       "0              2174             0              40  United-States       0  \n",
       "1                 0             0              13  United-States       0  \n",
       "2                 0             0              40  United-States       0  \n",
       "3                 0             0              40  United-States       0  \n",
       "4                 0             0              40           Cuba       0  \n",
       "5                 0             0              40  United-States       0  \n",
       "6                 0             0              16        Jamaica       0  \n",
       "7                 0             0              45  United-States       1  \n",
       "8             14084             0              50  United-States       1  \n",
       "9              5178             0              40  United-States       1  \n",
       "10                0             0              80  United-States       1  \n",
       "11                0             0              40          India       1  \n",
       "12                0             0              30  United-States       0  \n",
       "13                0             0              50  United-States       0  \n",
       "14                0             0              40  United-States       1  \n",
       "15                0             0              45         Mexico       0  \n",
       "16                0             0              35  United-States       0  \n",
       "17                0             0              40  United-States       0  \n",
       "18                0             0              50  United-States       0  \n",
       "19                0             0              45  United-States       1  \n",
       "20                0             0              60  United-States       1  \n",
       "21                0             0              20  United-States       0  \n",
       "22                0             0              40  United-States       0  \n",
       "23                0          2042              40  United-States       0  \n",
       "24                0             0              40  United-States       0  \n",
       "25                0             0              40  United-States       1  \n",
       "26                0             0              40  United-States       0  \n",
       "27                0             0              60          South       1  \n",
       "28                0             0              80  United-States       0  \n",
       "29                0             0              40  United-States       0  \n",
       "...             ...           ...             ...            ...     ...  \n",
       "16251          2936             0              20  United-States       0  \n",
       "16252          7688             0              40  United-States       1  \n",
       "16253             0          1669              40  United-States       0  \n",
       "16254             0             0              50  United-States       1  \n",
       "16255             0          1902              50  United-States       1  \n",
       "16256             0             0              40  United-States       0  \n",
       "16257             0             0              40  United-States       0  \n",
       "16258             0             0              40  United-States       0  \n",
       "16259             0             0               8  United-States       0  \n",
       "16260             0             0              45  United-States       0  \n",
       "16261         15024             0              55  United-States       1  \n",
       "16262             0             0              40  United-States       0  \n",
       "16263             0             0              40  United-States       0  \n",
       "16264             0             0              32  United-States       0  \n",
       "16265             0             0              40  United-States       1  \n",
       "16266             0             0              32  United-States       0  \n",
       "16267             0             0              40  United-States       0  \n",
       "16268             0             0              36  United-States       0  \n",
       "16269             0             0              40  United-States       0  \n",
       "16270             0             0              40  United-States       0  \n",
       "16271             0             0              48  United-States       0  \n",
       "16272             0             0              40  United-States       0  \n",
       "16273             0             0              40  United-States       0  \n",
       "16274             0             0              40  United-States       0  \n",
       "16275             0             0              40  United-States       0  \n",
       "16276             0             0              36  United-States       0  \n",
       "16277             0             0              40  United-States       0  \n",
       "16278             0             0              50  United-States       0  \n",
       "16279          5455             0              40  United-States       0  \n",
       "16280             0             0              60  United-States       1  \n",
       "\n",
       "[48842 rows x 13 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Education与Education_num是一个一一对应的关系 所以只需要保留一个属性就可以了\n",
    "all_data = all_data.drop('Education',axis=1,inplace=False)\n",
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#进行特征因子化\n",
    "#首先对Sex进行one-hot编码\n",
    "dummies_Sex = pd.get_dummies(all_data['Sex'],prefix='Sex')\n",
    "#对Workclass进行one-hot编码\n",
    "dummies_Workclass = pd.get_dummies(all_data['Workclass'],prefix='Wc')\n",
    "#对Martial_Status进行one-hot编码\n",
    "dummies_Mar_Sta = pd.get_dummies(all_data['Martial_Status'],prefix='MS')\n",
    "#对Occupation进行one-hot编码\n",
    "dummies_Occ = pd.get_dummies(all_data['Occupation'],prefix='Occ')\n",
    "#对Relationship进行one-hot编码\n",
    "dummies_Rel = pd.get_dummies(all_data['Relationship'],prefix='Rel')\n",
    "#对Race进行one-hot编码\n",
    "dummies_Race = pd.get_dummies(all_data['Race'],prefix='Race')\n",
    "#对Country进行one-hot编码\n",
    "dummies_Country = pd.get_dummies(all_data['Country'],prefix='Cou')\n",
    "\n",
    "all_data = pd.concat([all_data,dummies_Sex,dummies_Workclass,dummies_Mar_Sta,dummies_Occ,dummies_Rel,dummies_Race,dummies_Country],axis=1)\n",
    "all_data_df = all_data.drop(['Sex','Workclass','Martial_Status','Occupation','Relationship','Race','Country'],axis=1,inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Target</th>\n",
       "      <th>Sex_ Female</th>\n",
       "      <th>Sex_ Male</th>\n",
       "      <th>Wc_ Federal-gov</th>\n",
       "      <th>Wc_ Local-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>Cou_ Portugal</th>\n",
       "      <th>Cou_ Puerto-Rico</th>\n",
       "      <th>Cou_ Scotland</th>\n",
       "      <th>Cou_ South</th>\n",
       "      <th>Cou_ Taiwan</th>\n",
       "      <th>Cou_ Thailand</th>\n",
       "      <th>Cou_ Trinadad&amp;Tobago</th>\n",
       "      <th>Cou_ Vietnam</th>\n",
       "      <th>Cou_ Yugoslavia</th>\n",
       "      <th>Cou_United-States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>11</td>\n",
       "      <td>2936</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Education_Num  Capital_Gain  Capital_Loss  Hours_per_week  Target  \\\n",
       "0       39             13          2174             0              40       0   \n",
       "1       50             13             0             0              13       0   \n",
       "2       38              9             0             0              40       0   \n",
       "3       53              7             0             0              40       0   \n",
       "4       28             13             0             0              40       0   \n",
       "5       37             14             0             0              40       0   \n",
       "6       49              5             0             0              16       0   \n",
       "7       52              9             0             0              45       1   \n",
       "8       31             14         14084             0              50       1   \n",
       "9       42             13          5178             0              40       1   \n",
       "10      37             10             0             0              80       1   \n",
       "11      30             13             0             0              40       1   \n",
       "12      23             13             0             0              30       0   \n",
       "13      32             12             0             0              50       0   \n",
       "14      40             11             0             0              40       1   \n",
       "15      34              4             0             0              45       0   \n",
       "16      25              9             0             0              35       0   \n",
       "17      32              9             0             0              40       0   \n",
       "18      38              7             0             0              50       0   \n",
       "19      43             14             0             0              45       1   \n",
       "20      40             16             0             0              60       1   \n",
       "21      54              9             0             0              20       0   \n",
       "22      35              5             0             0              40       0   \n",
       "23      43              7             0          2042              40       0   \n",
       "24      59              9             0             0              40       0   \n",
       "25      56             13             0             0              40       1   \n",
       "26      19              9             0             0              40       0   \n",
       "27      54             10             0             0              60       1   \n",
       "28      39              9             0             0              80       0   \n",
       "29      49              9             0             0              40       0   \n",
       "...    ...            ...           ...           ...             ...     ...   \n",
       "16251   81             11          2936             0              20       0   \n",
       "16252   60             11          7688             0              40       1   \n",
       "16253   39             13             0          1669              40       0   \n",
       "16254   38             14             0             0              50       1   \n",
       "16255   43             14             0          1902              50       1   \n",
       "16256   23              9             0             0              40       0   \n",
       "16257   73             10             0             0              40       0   \n",
       "16258   35             10             0             0              40       0   \n",
       "16259   66              9             0             0               8       0   \n",
       "16260   27             10             0             0              45       0   \n",
       "16261   40             15         15024             0              55       1   \n",
       "16262   51              9             0             0              40       0   \n",
       "16263   22             10             0             0              40       0   \n",
       "16264   64              9             0             0              32       0   \n",
       "16265   50             14             0             0              40       1   \n",
       "16266   55              9             0             0              32       0   \n",
       "16267   38             11             0             0              40       0   \n",
       "16268   58             12             0             0              36       0   \n",
       "16269   32              9             0             0              40       0   \n",
       "16270   48              9             0             0              40       0   \n",
       "16271   61              9             0             0              48       0   \n",
       "16272   31              9             0             0              40       0   \n",
       "16273   25              9             0             0              40       0   \n",
       "16274   48             14             0             0              40       0   \n",
       "16275   33             13             0             0              40       0   \n",
       "16276   39             13             0             0              36       0   \n",
       "16277   64              9             0             0              40       0   \n",
       "16278   38             13             0             0              50       0   \n",
       "16279   44             13          5455             0              40       0   \n",
       "16280   35             13             0             0              60       1   \n",
       "\n",
       "       Sex_ Female  Sex_ Male  Wc_ Federal-gov  Wc_ Local-gov  \\\n",
       "0                0          1                0              0   \n",
       "1                0          1                0              0   \n",
       "2                0          1                0              0   \n",
       "3                0          1                0              0   \n",
       "4                1          0                0              0   \n",
       "5                1          0                0              0   \n",
       "6                1          0                0              0   \n",
       "7                0          1                0              0   \n",
       "8                1          0                0              0   \n",
       "9                0          1                0              0   \n",
       "10               0          1                0              0   \n",
       "11               0          1                0              0   \n",
       "12               1          0                0              0   \n",
       "13               0          1                0              0   \n",
       "14               0          1                0              0   \n",
       "15               0          1                0              0   \n",
       "16               0          1                0              0   \n",
       "17               0          1                0              0   \n",
       "18               0          1                0              0   \n",
       "19               1          0                0              0   \n",
       "20               0          1                0              0   \n",
       "21               1          0                0              0   \n",
       "22               0          1                1              0   \n",
       "23               0          1                0              0   \n",
       "24               1          0                0              0   \n",
       "25               0          1                0              1   \n",
       "26               0          1                0              0   \n",
       "27               0          1                0              0   \n",
       "28               0          1                0              0   \n",
       "29               0          1                0              0   \n",
       "...            ...        ...              ...            ...   \n",
       "16251            0          1                0              0   \n",
       "16252            0          1                0              0   \n",
       "16253            1          0                0              0   \n",
       "16254            0          1                0              0   \n",
       "16255            0          1                0              1   \n",
       "16256            0          1                0              0   \n",
       "16257            1          0                0              0   \n",
       "16258            0          1                0              0   \n",
       "16259            1          0                0              0   \n",
       "16260            1          0                0              0   \n",
       "16261            0          1                0              0   \n",
       "16262            0          1                0              0   \n",
       "16263            0          1                0              0   \n",
       "16264            0          1                0              0   \n",
       "16265            1          0                0              1   \n",
       "16266            1          0                0              0   \n",
       "16267            1          0                0              0   \n",
       "16268            0          1                0              0   \n",
       "16269            0          1                0              0   \n",
       "16270            0          1                0              0   \n",
       "16271            0          1                0              0   \n",
       "16272            0          1                0              0   \n",
       "16273            1          0                0              0   \n",
       "16274            0          1                0              1   \n",
       "16275            0          1                0              0   \n",
       "16276            1          0                0              0   \n",
       "16277            0          1                0              0   \n",
       "16278            0          1                0              0   \n",
       "16279            0          1                0              0   \n",
       "16280            0          1                0              0   \n",
       "\n",
       "             ...          Cou_ Portugal  Cou_ Puerto-Rico  Cou_ Scotland  \\\n",
       "0            ...                      0                 0              0   \n",
       "1            ...                      0                 0              0   \n",
       "2            ...                      0                 0              0   \n",
       "3            ...                      0                 0              0   \n",
       "4            ...                      0                 0              0   \n",
       "5            ...                      0                 0              0   \n",
       "6            ...                      0                 0              0   \n",
       "7            ...                      0                 0              0   \n",
       "8            ...                      0                 0              0   \n",
       "9            ...                      0                 0              0   \n",
       "10           ...                      0                 0              0   \n",
       "11           ...                      0                 0              0   \n",
       "12           ...                      0                 0              0   \n",
       "13           ...                      0                 0              0   \n",
       "14           ...                      0                 0              0   \n",
       "15           ...                      0                 0              0   \n",
       "16           ...                      0                 0              0   \n",
       "17           ...                      0                 0              0   \n",
       "18           ...                      0                 0              0   \n",
       "19           ...                      0                 0              0   \n",
       "20           ...                      0                 0              0   \n",
       "21           ...                      0                 0              0   \n",
       "22           ...                      0                 0              0   \n",
       "23           ...                      0                 0              0   \n",
       "24           ...                      0                 0              0   \n",
       "25           ...                      0                 0              0   \n",
       "26           ...                      0                 0              0   \n",
       "27           ...                      0                 0              0   \n",
       "28           ...                      0                 0              0   \n",
       "29           ...                      0                 0              0   \n",
       "...          ...                    ...               ...            ...   \n",
       "16251        ...                      0                 0              0   \n",
       "16252        ...                      0                 0              0   \n",
       "16253        ...                      0                 0              0   \n",
       "16254        ...                      0                 0              0   \n",
       "16255        ...                      0                 0              0   \n",
       "16256        ...                      0                 0              0   \n",
       "16257        ...                      0                 0              0   \n",
       "16258        ...                      0                 0              0   \n",
       "16259        ...                      0                 0              0   \n",
       "16260        ...                      0                 0              0   \n",
       "16261        ...                      0                 0              0   \n",
       "16262        ...                      0                 0              0   \n",
       "16263        ...                      0                 0              0   \n",
       "16264        ...                      0                 0              0   \n",
       "16265        ...                      0                 0              0   \n",
       "16266        ...                      0                 0              0   \n",
       "16267        ...                      0                 0              0   \n",
       "16268        ...                      0                 0              0   \n",
       "16269        ...                      0                 0              0   \n",
       "16270        ...                      0                 0              0   \n",
       "16271        ...                      0                 0              0   \n",
       "16272        ...                      0                 0              0   \n",
       "16273        ...                      0                 0              0   \n",
       "16274        ...                      0                 0              0   \n",
       "16275        ...                      0                 0              0   \n",
       "16276        ...                      0                 0              0   \n",
       "16277        ...                      0                 0              0   \n",
       "16278        ...                      0                 0              0   \n",
       "16279        ...                      0                 0              0   \n",
       "16280        ...                      0                 0              0   \n",
       "\n",
       "       Cou_ South  Cou_ Taiwan  Cou_ Thailand  Cou_ Trinadad&Tobago  \\\n",
       "0               0            0              0                     0   \n",
       "1               0            0              0                     0   \n",
       "2               0            0              0                     0   \n",
       "3               0            0              0                     0   \n",
       "4               0            0              0                     0   \n",
       "5               0            0              0                     0   \n",
       "6               0            0              0                     0   \n",
       "7               0            0              0                     0   \n",
       "8               0            0              0                     0   \n",
       "9               0            0              0                     0   \n",
       "10              0            0              0                     0   \n",
       "11              0            0              0                     0   \n",
       "12              0            0              0                     0   \n",
       "13              0            0              0                     0   \n",
       "14              0            0              0                     0   \n",
       "15              0            0              0                     0   \n",
       "16              0            0              0                     0   \n",
       "17              0            0              0                     0   \n",
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       "27              1            0              0                     0   \n",
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       "29              0            0              0                     0   \n",
       "...           ...          ...            ...                   ...   \n",
       "16251           0            0              0                     0   \n",
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       "16280           0            0              0                     0   \n",
       "\n",
       "       Cou_ Vietnam  Cou_ Yugoslavia  Cou_United-States  \n",
       "0                 0                0                  1  \n",
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       "...             ...              ...                ...  \n",
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       "\n",
       "[48842 rows x 89 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_target = all_data_df.pop(\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "24       0\n",
       "25       1\n",
       "26       0\n",
       "27       1\n",
       "28       0\n",
       "29       0\n",
       "        ..\n",
       "16251    0\n",
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       "16255    1\n",
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       "16259    0\n",
       "16260    0\n",
       "16261    1\n",
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       "16265    1\n",
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       "16269    0\n",
       "16270    0\n",
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       "Name: Target, Length: 48842, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据集分回训练集和测试集\n",
    "# dummy_train_data = all_data_df.loc[train_data.index]\n",
    "# dummy_test_data = all_data_df.loc[test_data.index]\n",
    "dummy_train_data_df = all_data_df[0:32561]\n",
    "dummy_test_data_df = all_data_df[32561:len(all_data_df)]\n",
    "dummy_train_data_target = y_target[0:32561]\n",
    "dummy_test_data_target = y_target[32561:len(all_data_df)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Sex_ Female</th>\n",
       "      <th>Sex_ Male</th>\n",
       "      <th>Wc_ Federal-gov</th>\n",
       "      <th>Wc_ Local-gov</th>\n",
       "      <th>Wc_ Never-worked</th>\n",
       "      <th>...</th>\n",
       "      <th>Cou_ Portugal</th>\n",
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       "      <th>Cou_ Thailand</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>23</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
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       "      <td>32</td>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>15020</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Education_Num  Capital_Gain  Capital_Loss  Hours_per_week  \\\n",
       "0       39             13          2174             0              40   \n",
       "1       50             13             0             0              13   \n",
       "2       38              9             0             0              40   \n",
       "3       53              7             0             0              40   \n",
       "4       28             13             0             0              40   \n",
       "5       37             14             0             0              40   \n",
       "6       49              5             0             0              16   \n",
       "7       52              9             0             0              45   \n",
       "8       31             14         14084             0              50   \n",
       "9       42             13          5178             0              40   \n",
       "10      37             10             0             0              80   \n",
       "11      30             13             0             0              40   \n",
       "12      23             13             0             0              30   \n",
       "13      32             12             0             0              50   \n",
       "14      40             11             0             0              40   \n",
       "15      34              4             0             0              45   \n",
       "16      25              9             0             0              35   \n",
       "17      32              9             0             0              40   \n",
       "18      38              7             0             0              50   \n",
       "19      43             14             0             0              45   \n",
       "20      40             16             0             0              60   \n",
       "21      54              9             0             0              20   \n",
       "22      35              5             0             0              40   \n",
       "23      43              7             0          2042              40   \n",
       "24      59              9             0             0              40   \n",
       "25      56             13             0             0              40   \n",
       "26      19              9             0             0              40   \n",
       "27      54             10             0             0              60   \n",
       "28      39              9             0             0              80   \n",
       "29      49              9             0             0              40   \n",
       "...    ...            ...           ...           ...             ...   \n",
       "32531   30             13             0             0              99   \n",
       "32532   34             16             0             0              60   \n",
       "32533   54             13             0             0              50   \n",
       "32534   37             10             0             0              39   \n",
       "32535   22              8             0             0              35   \n",
       "32536   34             13             0             0              55   \n",
       "32537   30              9             0             0              46   \n",
       "32538   38             13         15020             0              45   \n",
       "32539   71             16             0             0              10   \n",
       "32540   45              9             0             0              40   \n",
       "32541   41              9             0             0              32   \n",
       "32542   72              9             0             0              25   \n",
       "32543   45             12             0             0              48   \n",
       "32544   31             14             0             0              30   \n",
       "32545   39             12             0             0              20   \n",
       "32546   37             12             0             0              40   \n",
       "32547   43              9             0             0              40   \n",
       "32548   65             15          1086             0              60   \n",
       "32549   43             10             0             0              40   \n",
       "32550   43             10             0             0              50   \n",
       "32551   32              6             0             0              40   \n",
       "32552   43             11             0             0              45   \n",
       "32553   32             14             0             0              11   \n",
       "32554   53             14             0             0              40   \n",
       "32555   22             10             0             0              40   \n",
       "32556   27             12             0             0              38   \n",
       "32557   40              9             0             0              40   \n",
       "32558   58              9             0             0              40   \n",
       "32559   22              9             0             0              20   \n",
       "32560   52              9         15024             0              40   \n",
       "\n",
       "       Sex_ Female  Sex_ Male  Wc_ Federal-gov  Wc_ Local-gov  \\\n",
       "0                0          1                0              0   \n",
       "1                0          1                0              0   \n",
       "2                0          1                0              0   \n",
       "3                0          1                0              0   \n",
       "4                1          0                0              0   \n",
       "5                1          0                0              0   \n",
       "6                1          0                0              0   \n",
       "7                0          1                0              0   \n",
       "8                1          0                0              0   \n",
       "9                0          1                0              0   \n",
       "10               0          1                0              0   \n",
       "11               0          1                0              0   \n",
       "12               1          0                0              0   \n",
       "13               0          1                0              0   \n",
       "14               0          1                0              0   \n",
       "15               0          1                0              0   \n",
       "16               0          1                0              0   \n",
       "17               0          1                0              0   \n",
       "18               0          1                0              0   \n",
       "19               1          0                0              0   \n",
       "20               0          1                0              0   \n",
       "21               1          0                0              0   \n",
       "22               0          1                1              0   \n",
       "23               0          1                0              0   \n",
       "24               1          0                0              0   \n",
       "25               0          1                0              1   \n",
       "26               0          1                0              0   \n",
       "27               0          1                0              0   \n",
       "28               0          1                0              0   \n",
       "29               0          1                0              0   \n",
       "...            ...        ...              ...            ...   \n",
       "32531            1          0                0              0   \n",
       "32532            0          1                0              0   \n",
       "32533            0          1                0              0   \n",
       "32534            1          0                0              0   \n",
       "32535            0          1                0              0   \n",
       "32536            1          0                0              0   \n",
       "32537            0          1                0              0   \n",
       "32538            1          0                0              0   \n",
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       "32558            1          0                0              0   \n",
       "32559            0          1                0              0   \n",
       "32560            1          0                0              0   \n",
       "\n",
       "       Wc_ Never-worked        ...          Cou_ Portugal  Cou_ Puerto-Rico  \\\n",
       "0                     0        ...                      0                 0   \n",
       "1                     0        ...                      0                 0   \n",
       "2                     0        ...                      0                 0   \n",
       "3                     0        ...                      0                 0   \n",
       "4                     0        ...                      0                 0   \n",
       "5                     0        ...                      0                 0   \n",
       "6                     0        ...                      0                 0   \n",
       "7                     0        ...                      0                 0   \n",
       "8                     0        ...                      0                 0   \n",
       "9                     0        ...                      0                 0   \n",
       "10                    0        ...                      0                 0   \n",
       "11                    0        ...                      0                 0   \n",
       "12                    0        ...                      0                 0   \n",
       "13                    0        ...                      0                 0   \n",
       "14                    0        ...                      0                 0   \n",
       "15                    0        ...                      0                 0   \n",
       "16                    0        ...                      0                 0   \n",
       "17                    0        ...                      0                 0   \n",
       "18                    0        ...                      0                 0   \n",
       "19                    0        ...                      0                 0   \n",
       "20                    0        ...                      0                 0   \n",
       "21                    0        ...                      0                 0   \n",
       "22                    0        ...                      0                 0   \n",
       "23                    0        ...                      0                 0   \n",
       "24                    0        ...                      0                 0   \n",
       "25                    0        ...                      0                 0   \n",
       "26                    0        ...                      0                 0   \n",
       "27                    0        ...                      0                 0   \n",
       "28                    0        ...                      0                 0   \n",
       "29                    0        ...                      0                 0   \n",
       "...                 ...        ...                    ...               ...   \n",
       "32531                 0        ...                      0                 0   \n",
       "32532                 0        ...                      0                 0   \n",
       "32533                 0        ...                      0                 0   \n",
       "32534                 0        ...                      0                 0   \n",
       "32535                 0        ...                      0                 0   \n",
       "32536                 0        ...                      0                 0   \n",
       "32537                 0        ...                      0                 0   \n",
       "32538                 0        ...                      0                 0   \n",
       "32539                 0        ...                      0                 0   \n",
       "32540                 0        ...                      0                 0   \n",
       "32541                 0        ...                      0                 0   \n",
       "32542                 0        ...                      0                 0   \n",
       "32543                 0        ...                      0                 0   \n",
       "32544                 0        ...                      0                 0   \n",
       "32545                 0        ...                      0                 0   \n",
       "32546                 0        ...                      0                 0   \n",
       "32547                 0        ...                      0                 0   \n",
       "32548                 0        ...                      0                 0   \n",
       "32549                 0        ...                      0                 0   \n",
       "32550                 0        ...                      0                 0   \n",
       "32551                 0        ...                      0                 0   \n",
       "32552                 0        ...                      0                 0   \n",
       "32553                 0        ...                      0                 0   \n",
       "32554                 0        ...                      0                 0   \n",
       "32555                 0        ...                      0                 0   \n",
       "32556                 0        ...                      0                 0   \n",
       "32557                 0        ...                      0                 0   \n",
       "32558                 0        ...                      0                 0   \n",
       "32559                 0        ...                      0                 0   \n",
       "32560                 0        ...                      0                 0   \n",
       "\n",
       "       Cou_ Scotland  Cou_ South  Cou_ Taiwan  Cou_ Thailand  \\\n",
       "0                  0           0            0              0   \n",
       "1                  0           0            0              0   \n",
       "2                  0           0            0              0   \n",
       "3                  0           0            0              0   \n",
       "4                  0           0            0              0   \n",
       "5                  0           0            0              0   \n",
       "6                  0           0            0              0   \n",
       "7                  0           0            0              0   \n",
       "8                  0           0            0              0   \n",
       "9                  0           0            0              0   \n",
       "10                 0           0            0              0   \n",
       "11                 0           0            0              0   \n",
       "12                 0           0            0              0   \n",
       "13                 0           0            0              0   \n",
       "14                 0           0            0              0   \n",
       "15                 0           0            0              0   \n",
       "16                 0           0            0              0   \n",
       "17                 0           0            0              0   \n",
       "18                 0           0            0              0   \n",
       "19                 0           0            0              0   \n",
       "20                 0           0            0              0   \n",
       "21                 0           0            0              0   \n",
       "22                 0           0            0              0   \n",
       "23                 0           0            0              0   \n",
       "24                 0           0            0              0   \n",
       "25                 0           0            0              0   \n",
       "26                 0           0            0              0   \n",
       "27                 0           1            0              0   \n",
       "28                 0           0            0              0   \n",
       "29                 0           0            0              0   \n",
       "...              ...         ...          ...            ...   \n",
       "32531              0           0            0              0   \n",
       "32532              0           0            0              0   \n",
       "32533              0           0            0              0   \n",
       "32534              0           0            0              0   \n",
       "32535              0           0            0              0   \n",
       "32536              0           0            0              0   \n",
       "32537              0           0            0              0   \n",
       "32538              0           0            0              0   \n",
       "32539              0           0            0              0   \n",
       "32540              0           0            0              0   \n",
       "32541              0           0            0              0   \n",
       "32542              0           0            0              0   \n",
       "32543              0           0            0              0   \n",
       "32544              0           0            0              0   \n",
       "32545              0           0            0              0   \n",
       "32546              0           0            0              0   \n",
       "32547              0           0            0              0   \n",
       "32548              0           0            0              0   \n",
       "32549              0           0            0              0   \n",
       "32550              0           0            0              0   \n",
       "32551              0           0            0              0   \n",
       "32552              0           0            0              0   \n",
       "32553              0           0            1              0   \n",
       "32554              0           0            0              0   \n",
       "32555              0           0            0              0   \n",
       "32556              0           0            0              0   \n",
       "32557              0           0            0              0   \n",
       "32558              0           0            0              0   \n",
       "32559              0           0            0              0   \n",
       "32560              0           0            0              0   \n",
       "\n",
       "       Cou_ Trinadad&Tobago  Cou_ Vietnam  Cou_ Yugoslavia  Cou_United-States  \n",
       "0                         0             0                0                  1  \n",
       "1                         0             0                0                  1  \n",
       "2                         0             0                0                  1  \n",
       "3                         0             0                0                  1  \n",
       "4                         0             0                0                  0  \n",
       "5                         0             0                0                  1  \n",
       "6                         0             0                0                  0  \n",
       "7                         0             0                0                  1  \n",
       "8                         0             0                0                  1  \n",
       "9                         0             0                0                  1  \n",
       "10                        0             0                0                  1  \n",
       "11                        0             0                0                  0  \n",
       "12                        0             0                0                  1  \n",
       "13                        0             0                0                  1  \n",
       "14                        0             0                0                  1  \n",
       "15                        0             0                0                  0  \n",
       "16                        0             0                0                  1  \n",
       "17                        0             0                0                  1  \n",
       "18                        0             0                0                  1  \n",
       "19                        0             0                0                  1  \n",
       "20                        0             0                0                  1  \n",
       "21                        0             0                0                  1  \n",
       "22                        0             0                0                  1  \n",
       "23                        0             0                0                  1  \n",
       "24                        0             0                0                  1  \n",
       "25                        0             0                0                  1  \n",
       "26                        0             0                0                  1  \n",
       "27                        0             0                0                  0  \n",
       "28                        0             0                0                  1  \n",
       "29                        0             0                0                  1  \n",
       "...                     ...           ...              ...                ...  \n",
       "32531                     0             0                0                  1  \n",
       "32532                     0             0                0                  1  \n",
       "32533                     0             0                0                  0  \n",
       "32534                     0             0                0                  1  \n",
       "32535                     0             0                0                  1  \n",
       "32536                     0             0                0                  1  \n",
       "32537                     0             0                0                  1  \n",
       "32538                     0             0                0                  1  \n",
       "32539                     0             0                0                  1  \n",
       "32540                     0             0                0                  1  \n",
       "32541                     0             0                0                  1  \n",
       "32542                     0             0                0                  1  \n",
       "32543                     0             0                0                  1  \n",
       "32544                     0             0                0                  1  \n",
       "32545                     0             0                0                  1  \n",
       "32546                     0             0                0                  1  \n",
       "32547                     0             0                0                  0  \n",
       "32548                     0             0                0                  1  \n",
       "32549                     0             0                0                  1  \n",
       "32550                     0             0                0                  1  \n",
       "32551                     0             0                0                  1  \n",
       "32552                     0             0                0                  1  \n",
       "32553                     0             0                0                  0  \n",
       "32554                     0             0                0                  1  \n",
       "32555                     0             0                0                  1  \n",
       "32556                     0             0                0                  1  \n",
       "32557                     0             0                0                  1  \n",
       "32558                     0             0                0                  1  \n",
       "32559                     0             0                0                  1  \n",
       "32560                     0             0                0                  1  \n",
       "\n",
       "[32561 rows x 88 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练数据的特征\n",
    "dummy_train_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "5        0\n",
       "6        0\n",
       "7        1\n",
       "8        1\n",
       "9        1\n",
       "10       1\n",
       "11       1\n",
       "12       0\n",
       "13       0\n",
       "14       1\n",
       "15       0\n",
       "16       0\n",
       "17       0\n",
       "18       0\n",
       "19       1\n",
       "20       1\n",
       "21       0\n",
       "22       0\n",
       "23       0\n",
       "24       0\n",
       "25       1\n",
       "26       0\n",
       "27       1\n",
       "28       0\n",
       "29       0\n",
       "        ..\n",
       "32531    0\n",
       "32532    1\n",
       "32533    1\n",
       "32534    0\n",
       "32535    0\n",
       "32536    1\n",
       "32537    0\n",
       "32538    1\n",
       "32539    1\n",
       "32540    0\n",
       "32541    0\n",
       "32542    0\n",
       "32543    0\n",
       "32544    0\n",
       "32545    1\n",
       "32546    0\n",
       "32547    0\n",
       "32548    0\n",
       "32549    0\n",
       "32550    0\n",
       "32551    0\n",
       "32552    0\n",
       "32553    0\n",
       "32554    1\n",
       "32555    0\n",
       "32556    0\n",
       "32557    1\n",
       "32558    0\n",
       "32559    0\n",
       "32560    1\n",
       "Name: Target, Length: 32561, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练数据的标签\n",
    "dummy_train_data_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Num</th>\n",
       "      <th>Capital_Gain</th>\n",
       "      <th>Capital_Loss</th>\n",
       "      <th>Hours_per_week</th>\n",
       "      <th>Sex_ Female</th>\n",
       "      <th>Sex_ Male</th>\n",
       "      <th>Wc_ Federal-gov</th>\n",
       "      <th>Wc_ Local-gov</th>\n",
       "      <th>Wc_ Never-worked</th>\n",
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       "      <th>Cou_ Taiwan</th>\n",
       "      <th>Cou_ Thailand</th>\n",
       "      <th>Cou_ Trinadad&amp;Tobago</th>\n",
       "      <th>Cou_ Vietnam</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <td>10</td>\n",
       "      <td>7688</td>\n",
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       "      <td>...</td>\n",
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       "      <td>10</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>15</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "      <td>6418</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>11</td>\n",
       "      <td>2936</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16281 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Education_Num  Capital_Gain  Capital_Loss  Hours_per_week  \\\n",
       "0       25              7             0             0              40   \n",
       "1       38              9             0             0              50   \n",
       "2       28             12             0             0              40   \n",
       "3       44             10          7688             0              40   \n",
       "4       18             10             0             0              30   \n",
       "5       34              6             0             0              30   \n",
       "6       29              9             0             0              40   \n",
       "7       63             15          3103             0              32   \n",
       "8       24             10             0             0              40   \n",
       "9       55              4             0             0              10   \n",
       "10      65              9          6418             0              40   \n",
       "11      36             13             0             0              40   \n",
       "12      26              9             0             0              39   \n",
       "13      58              9             0             0              35   \n",
       "14      48              9          3103             0              48   \n",
       "15      43             14             0             0              50   \n",
       "16      20             10             0             0              25   \n",
       "17      43              9             0             0              30   \n",
       "18      37              9             0             0              20   \n",
       "19      40             16             0             0              45   \n",
       "20      34             13             0             0              47   \n",
       "21      34             10             0             0              35   \n",
       "22      72              4             0             0               6   \n",
       "23      25             13             0             0              43   \n",
       "24      25             13             0             0              40   \n",
       "25      45              9          7298             0              90   \n",
       "26      22              9             0             0              20   \n",
       "27      23              9             0             0              54   \n",
       "28      54              9             0             0              35   \n",
       "29      32             10             0             0              60   \n",
       "...    ...            ...           ...           ...             ...   \n",
       "16251   81             11          2936             0              20   \n",
       "16252   60             11          7688             0              40   \n",
       "16253   39             13             0          1669              40   \n",
       "16254   38             14             0             0              50   \n",
       "16255   43             14             0          1902              50   \n",
       "16256   23              9             0             0              40   \n",
       "16257   73             10             0             0              40   \n",
       "16258   35             10             0             0              40   \n",
       "16259   66              9             0             0               8   \n",
       "16260   27             10             0             0              45   \n",
       "16261   40             15         15024             0              55   \n",
       "16262   51              9             0             0              40   \n",
       "16263   22             10             0             0              40   \n",
       "16264   64              9             0             0              32   \n",
       "16265   50             14             0             0              40   \n",
       "16266   55              9             0             0              32   \n",
       "16267   38             11             0             0              40   \n",
       "16268   58             12             0             0              36   \n",
       "16269   32              9             0             0              40   \n",
       "16270   48              9             0             0              40   \n",
       "16271   61              9             0             0              48   \n",
       "16272   31              9             0             0              40   \n",
       "16273   25              9             0             0              40   \n",
       "16274   48             14             0             0              40   \n",
       "16275   33             13             0             0              40   \n",
       "16276   39             13             0             0              36   \n",
       "16277   64              9             0             0              40   \n",
       "16278   38             13             0             0              50   \n",
       "16279   44             13          5455             0              40   \n",
       "16280   35             13             0             0              60   \n",
       "\n",
       "       Sex_ Female  Sex_ Male  Wc_ Federal-gov  Wc_ Local-gov  \\\n",
       "0                0          1                0              0   \n",
       "1                0          1                0              0   \n",
       "2                0          1                0              1   \n",
       "3                0          1                0              0   \n",
       "4                1          0                0              0   \n",
       "5                0          1                0              0   \n",
       "6                0          1                0              0   \n",
       "7                0          1                0              0   \n",
       "8                1          0                0              0   \n",
       "9                0          1                0              0   \n",
       "10               0          1                0              0   \n",
       "11               0          1                1              0   \n",
       "12               1          0                0              0   \n",
       "13               0          1                0              0   \n",
       "14               0          1                0              0   \n",
       "15               0          1                0              0   \n",
       "16               0          1                0              0   \n",
       "17               1          0                0              0   \n",
       "18               1          0                0              0   \n",
       "19               0          1                0              0   \n",
       "20               0          1                0              0   \n",
       "21               1          0                0              0   \n",
       "22               1          0                0              0   \n",
       "23               0          1                0              0   \n",
       "24               0          1                0              0   \n",
       "25               0          1                0              0   \n",
       "26               0          1                0              0   \n",
       "27               0          1                0              0   \n",
       "28               0          1                0              0   \n",
       "29               0          1                0              0   \n",
       "...            ...        ...              ...            ...   \n",
       "16251            0          1                0              0   \n",
       "16252            0          1                0              0   \n",
       "16253            1          0                0              0   \n",
       "16254            0          1                0              0   \n",
       "16255            0          1                0              1   \n",
       "16256            0          1                0              0   \n",
       "16257            1          0                0              0   \n",
       "16258            0          1                0              0   \n",
       "16259            1          0                0              0   \n",
       "16260            1          0                0              0   \n",
       "16261            0          1                0              0   \n",
       "16262            0          1                0              0   \n",
       "16263            0          1                0              0   \n",
       "16264            0          1                0              0   \n",
       "16265            1          0                0              1   \n",
       "16266            1          0                0              0   \n",
       "16267            1          0                0              0   \n",
       "16268            0          1                0              0   \n",
       "16269            0          1                0              0   \n",
       "16270            0          1                0              0   \n",
       "16271            0          1                0              0   \n",
       "16272            0          1                0              0   \n",
       "16273            1          0                0              0   \n",
       "16274            0          1                0              1   \n",
       "16275            0          1                0              0   \n",
       "16276            1          0                0              0   \n",
       "16277            0          1                0              0   \n",
       "16278            0          1                0              0   \n",
       "16279            0          1                0              0   \n",
       "16280            0          1                0              0   \n",
       "\n",
       "       Wc_ Never-worked        ...          Cou_ Portugal  Cou_ Puerto-Rico  \\\n",
       "0                     0        ...                      0                 0   \n",
       "1                     0        ...                      0                 0   \n",
       "2                     0        ...                      0                 0   \n",
       "3                     0        ...                      0                 0   \n",
       "4                     0        ...                      0                 0   \n",
       "5                     0        ...                      0                 0   \n",
       "6                     0        ...                      0                 0   \n",
       "7                     0        ...                      0                 0   \n",
       "8                     0        ...                      0                 0   \n",
       "9                     0        ...                      0                 0   \n",
       "10                    0        ...                      0                 0   \n",
       "11                    0        ...                      0                 0   \n",
       "12                    0        ...                      0                 0   \n",
       "13                    0        ...                      0                 0   \n",
       "14                    0        ...                      0                 0   \n",
       "15                    0        ...                      0                 0   \n",
       "16                    0        ...                      0                 0   \n",
       "17                    0        ...                      0                 0   \n",
       "18                    0        ...                      0                 0   \n",
       "19                    0        ...                      0                 0   \n",
       "20                    0        ...                      0                 0   \n",
       "21                    0        ...                      0                 0   \n",
       "22                    0        ...                      0                 0   \n",
       "23                    0        ...                      0                 0   \n",
       "24                    0        ...                      0                 0   \n",
       "25                    0        ...                      0                 0   \n",
       "26                    0        ...                      0                 0   \n",
       "27                    0        ...                      0                 0   \n",
       "28                    0        ...                      0                 0   \n",
       "29                    0        ...                      0                 0   \n",
       "...                 ...        ...                    ...               ...   \n",
       "16251                 0        ...                      0                 0   \n",
       "16252                 0        ...                      0                 0   \n",
       "16253                 0        ...                      0                 0   \n",
       "16254                 0        ...                      0                 0   \n",
       "16255                 0        ...                      0                 0   \n",
       "16256                 0        ...                      0                 0   \n",
       "16257                 0        ...                      0                 0   \n",
       "16258                 0        ...                      0                 0   \n",
       "16259                 0        ...                      0                 0   \n",
       "16260                 0        ...                      0                 0   \n",
       "16261                 0        ...                      0                 0   \n",
       "16262                 0        ...                      0                 0   \n",
       "16263                 0        ...                      0                 0   \n",
       "16264                 0        ...                      0                 0   \n",
       "16265                 0        ...                      0                 0   \n",
       "16266                 0        ...                      0                 0   \n",
       "16267                 0        ...                      0                 0   \n",
       "16268                 0        ...                      0                 0   \n",
       "16269                 0        ...                      0                 0   \n",
       "16270                 0        ...                      0                 0   \n",
       "16271                 0        ...                      0                 0   \n",
       "16272                 0        ...                      0                 0   \n",
       "16273                 0        ...                      0                 0   \n",
       "16274                 0        ...                      0                 0   \n",
       "16275                 0        ...                      0                 0   \n",
       "16276                 0        ...                      0                 0   \n",
       "16277                 0        ...                      0                 0   \n",
       "16278                 0        ...                      0                 0   \n",
       "16279                 0        ...                      0                 0   \n",
       "16280                 0        ...                      0                 0   \n",
       "\n",
       "       Cou_ Scotland  Cou_ South  Cou_ Taiwan  Cou_ Thailand  \\\n",
       "0                  0           0            0              0   \n",
       "1                  0           0            0              0   \n",
       "2                  0           0            0              0   \n",
       "3                  0           0            0              0   \n",
       "4                  0           0            0              0   \n",
       "5                  0           0            0              0   \n",
       "6                  0           0            0              0   \n",
       "7                  0           0            0              0   \n",
       "8                  0           0            0              0   \n",
       "9                  0           0            0              0   \n",
       "10                 0           0            0              0   \n",
       "11                 0           0            0              0   \n",
       "12                 0           0            0              0   \n",
       "13                 0           0            0              0   \n",
       "14                 0           0            0              0   \n",
       "15                 0           0            0              0   \n",
       "16                 0           0            0              0   \n",
       "17                 0           0            0              0   \n",
       "18                 0           0            0              0   \n",
       "19                 0           0            0              0   \n",
       "20                 0           0            0              0   \n",
       "21                 0           0            0              0   \n",
       "22                 0           0            0              0   \n",
       "23                 0           0            0              0   \n",
       "24                 0           0            0              0   \n",
       "25                 0           0            0              0   \n",
       "26                 0           0            0              0   \n",
       "27                 0           0            0              0   \n",
       "28                 0           0            0              0   \n",
       "29                 0           0            0              0   \n",
       "...              ...         ...          ...            ...   \n",
       "16251              0           0            0              0   \n",
       "16252              0           0            0              0   \n",
       "16253              0           0            0              0   \n",
       "16254              0           0            0              0   \n",
       "16255              0           0            0              0   \n",
       "16256              0           0            0              0   \n",
       "16257              0           0            0              0   \n",
       "16258              0           0            0              0   \n",
       "16259              0           0            0              0   \n",
       "16260              0           0            0              0   \n",
       "16261              0           0            0              0   \n",
       "16262              0           0            0              0   \n",
       "16263              0           0            0              0   \n",
       "16264              0           0            0              0   \n",
       "16265              0           0            0              0   \n",
       "16266              0           0            0              0   \n",
       "16267              0           0            0              0   \n",
       "16268              0           0            0              0   \n",
       "16269              0           0            0              0   \n",
       "16270              0           0            0              0   \n",
       "16271              0           0            0              0   \n",
       "16272              0           0            0              0   \n",
       "16273              0           0            0              0   \n",
       "16274              0           0            0              0   \n",
       "16275              0           0            0              0   \n",
       "16276              0           0            0              0   \n",
       "16277              0           0            0              0   \n",
       "16278              0           0            0              0   \n",
       "16279              0           0            0              0   \n",
       "16280              0           0            0              0   \n",
       "\n",
       "       Cou_ Trinadad&Tobago  Cou_ Vietnam  Cou_ Yugoslavia  Cou_United-States  \n",
       "0                         0             0                0                  1  \n",
       "1                         0             0                0                  1  \n",
       "2                         0             0                0                  1  \n",
       "3                         0             0                0                  1  \n",
       "4                         0             0                0                  1  \n",
       "5                         0             0                0                  1  \n",
       "6                         0             0                0                  1  \n",
       "7                         0             0                0                  1  \n",
       "8                         0             0                0                  1  \n",
       "9                         0             0                0                  1  \n",
       "10                        0             0                0                  1  \n",
       "11                        0             0                0                  1  \n",
       "12                        0             0                0                  1  \n",
       "13                        0             0                0                  1  \n",
       "14                        0             0                0                  1  \n",
       "15                        0             0                0                  1  \n",
       "16                        0             0                0                  1  \n",
       "17                        0             0                0                  1  \n",
       "18                        0             0                0                  1  \n",
       "19                        0             0                0                  1  \n",
       "20                        0             0                0                  1  \n",
       "21                        0             0                0                  1  \n",
       "22                        0             0                0                  1  \n",
       "23                        0             0                0                  0  \n",
       "24                        0             0                0                  1  \n",
       "25                        0             0                0                  1  \n",
       "26                        0             0                0                  1  \n",
       "27                        0             0                0                  1  \n",
       "28                        0             0                0                  1  \n",
       "29                        0             0                0                  1  \n",
       "...                     ...           ...              ...                ...  \n",
       "16251                     0             0                0                  1  \n",
       "16252                     0             0                0                  1  \n",
       "16253                     0             0                0                  1  \n",
       "16254                     0             0                0                  1  \n",
       "16255                     0             0                0                  1  \n",
       "16256                     0             0                0                  1  \n",
       "16257                     0             0                0                  1  \n",
       "16258                     0             0                0                  1  \n",
       "16259                     0             0                0                  1  \n",
       "16260                     0             0                0                  1  \n",
       "16261                     0             0                0                  1  \n",
       "16262                     0             0                0                  1  \n",
       "16263                     0             0                0                  1  \n",
       "16264                     0             0                0                  1  \n",
       "16265                     0             0                0                  1  \n",
       "16266                     0             0                0                  1  \n",
       "16267                     0             0                0                  1  \n",
       "16268                     0             0                0                  1  \n",
       "16269                     0             0                0                  1  \n",
       "16270                     0             0                0                  1  \n",
       "16271                     0             0                0                  1  \n",
       "16272                     0             0                0                  1  \n",
       "16273                     0             0                0                  1  \n",
       "16274                     0             0                0                  1  \n",
       "16275                     0             0                0                  1  \n",
       "16276                     0             0                0                  1  \n",
       "16277                     0             0                0                  1  \n",
       "16278                     0             0                0                  1  \n",
       "16279                     0             0                0                  1  \n",
       "16280                     0             0                0                  1  \n",
       "\n",
       "[16281 rows x 88 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试数据的特征\n",
    "dummy_test_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        1\n",
       "3        1\n",
       "4        0\n",
       "5        0\n",
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       "28       0\n",
       "29       0\n",
       "        ..\n",
       "16251    0\n",
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       "16254    1\n",
       "16255    1\n",
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       "16259    0\n",
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       "16280    1\n",
       "Name: Target, Length: 16281, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试数据的标签\n",
    "dummy_test_data_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
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       "      <td>1.134739</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-0.115955</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>3.204161</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.629143</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.142331</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.845327</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.482518</td>\n",
       "      <td>0.746039</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.103983</td>\n",
       "      <td>0.357340</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.335892</td>\n",
       "      <td>-2.363558</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.369519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.995706</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.440378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.482518</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.042642</td>\n",
       "      <td>-1.197459</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>1.523438</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.369519</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.103983</td>\n",
       "      <td>2.300838</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>1.584366</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.130359</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-1.655225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.262580</td>\n",
       "      <td>-1.974858</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>-1.197459</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>4.850916</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.496922</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.276984</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.435581</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.130359</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>1.584366</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.030671</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>3.204161</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.763796</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>-0.629143</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>4.742967</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>-0.335892</td>\n",
       "      <td>2.300838</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>1.584366</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>1.130359</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>-0.115955</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.116419</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>-1.215643</td>\n",
       "      <td>-0.808759</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.440378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>-0.335892</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>1.179417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>-0.629143</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.450509</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>-0.042642</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>1.887883</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.369519</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>2.376673</td>\n",
       "      <td>2.300838</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-2.465122</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>0.470546</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>0.177296</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.683348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>2.449986</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-1.250276</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>0.470546</td>\n",
       "      <td>0.746039</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.612489</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>-0.555830</td>\n",
       "      <td>1.523438</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.845327</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>0.030671</td>\n",
       "      <td>0.746039</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-1.655225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>-0.115955</td>\n",
       "      <td>0.746039</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>1.936798</td>\n",
       "      <td>1.912138</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>1.584366</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>-0.482518</td>\n",
       "      <td>-1.586158</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>0.323921</td>\n",
       "      <td>0.357340</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>0.369519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>-0.482518</td>\n",
       "      <td>1.523438</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-2.384133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>1.057047</td>\n",
       "      <td>1.523438</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>-1.215643</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>-0.849080</td>\n",
       "      <td>0.746039</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.197409</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>0.103983</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>1.423610</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>-1.215643</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-1.655225</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>0.983734</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>1.888424</td>\n",
       "      <td>-0.216660</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age  Education_Num  Capital_Gain  Capital_Loss  Hours_per_week  \\\n",
       "0      0.030671       1.134739      0.148453     -0.216660       -0.035429   \n",
       "1      0.837109       1.134739     -0.145920     -0.216660       -2.222153   \n",
       "2     -0.042642      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "3      1.057047      -1.197459     -0.145920     -0.216660       -0.035429   \n",
       "4     -0.775768       1.134739     -0.145920     -0.216660       -0.035429   \n",
       "5     -0.115955       1.523438     -0.145920     -0.216660       -0.035429   \n",
       "6      0.763796      -1.974858     -0.145920     -0.216660       -1.979184   \n",
       "7      0.983734      -0.420060     -0.145920     -0.216660        0.369519   \n",
       "8     -0.555830       1.523438      1.761142     -0.216660        0.774468   \n",
       "9      0.250608       1.134739      0.555214     -0.216660       -0.035429   \n",
       "10    -0.115955      -0.031360     -0.145920     -0.216660        3.204161   \n",
       "11    -0.629143       1.134739     -0.145920     -0.216660       -0.035429   \n",
       "12    -1.142331       1.134739     -0.145920     -0.216660       -0.845327   \n",
       "13    -0.482518       0.746039     -0.145920     -0.216660        0.774468   \n",
       "14     0.103983       0.357340     -0.145920     -0.216660       -0.035429   \n",
       "15    -0.335892      -2.363558     -0.145920     -0.216660        0.369519   \n",
       "16    -0.995706      -0.420060     -0.145920     -0.216660       -0.440378   \n",
       "17    -0.482518      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "18    -0.042642      -1.197459     -0.145920     -0.216660        0.774468   \n",
       "19     0.323921       1.523438     -0.145920     -0.216660        0.369519   \n",
       "20     0.103983       2.300838     -0.145920     -0.216660        1.584366   \n",
       "21     1.130359      -0.420060     -0.145920     -0.216660       -1.655225   \n",
       "22    -0.262580      -1.974858     -0.145920     -0.216660       -0.035429   \n",
       "23     0.323921      -1.197459     -0.145920      4.850916       -0.035429   \n",
       "24     1.496922      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "25     1.276984       1.134739     -0.145920     -0.216660       -0.035429   \n",
       "26    -1.435581      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "27     1.130359      -0.031360     -0.145920     -0.216660        1.584366   \n",
       "28     0.030671      -0.420060     -0.145920     -0.216660        3.204161   \n",
       "29     0.763796      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "...         ...            ...           ...           ...             ...   \n",
       "32531 -0.629143       1.134739     -0.145920     -0.216660        4.742967   \n",
       "32532 -0.335892       2.300838     -0.145920     -0.216660        1.584366   \n",
       "32533  1.130359       1.134739     -0.145920     -0.216660        0.774468   \n",
       "32534 -0.115955      -0.031360     -0.145920     -0.216660       -0.116419   \n",
       "32535 -1.215643      -0.808759     -0.145920     -0.216660       -0.440378   \n",
       "32536 -0.335892       1.134739     -0.145920     -0.216660        1.179417   \n",
       "32537 -0.629143      -0.420060     -0.145920     -0.216660        0.450509   \n",
       "32538 -0.042642       1.134739      1.887883     -0.216660        0.369519   \n",
       "32539  2.376673       2.300838     -0.145920     -0.216660       -2.465122   \n",
       "32540  0.470546      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "32541  0.177296      -0.420060     -0.145920     -0.216660       -0.683348   \n",
       "32542  2.449986      -0.420060     -0.145920     -0.216660       -1.250276   \n",
       "32543  0.470546       0.746039     -0.145920     -0.216660        0.612489   \n",
       "32544 -0.555830       1.523438     -0.145920     -0.216660       -0.845327   \n",
       "32545  0.030671       0.746039     -0.145920     -0.216660       -1.655225   \n",
       "32546 -0.115955       0.746039     -0.145920     -0.216660       -0.035429   \n",
       "32547  0.323921      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "32548  1.936798       1.912138      0.001131     -0.216660        1.584366   \n",
       "32549  0.323921      -0.031360     -0.145920     -0.216660       -0.035429   \n",
       "32550  0.323921      -0.031360     -0.145920     -0.216660        0.774468   \n",
       "32551 -0.482518      -1.586158     -0.145920     -0.216660       -0.035429   \n",
       "32552  0.323921       0.357340     -0.145920     -0.216660        0.369519   \n",
       "32553 -0.482518       1.523438     -0.145920     -0.216660       -2.384133   \n",
       "32554  1.057047       1.523438     -0.145920     -0.216660       -0.035429   \n",
       "32555 -1.215643      -0.031360     -0.145920     -0.216660       -0.035429   \n",
       "32556 -0.849080       0.746039     -0.145920     -0.216660       -0.197409   \n",
       "32557  0.103983      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "32558  1.423610      -0.420060     -0.145920     -0.216660       -0.035429   \n",
       "32559 -1.215643      -0.420060     -0.145920     -0.216660       -1.655225   \n",
       "32560  0.983734      -0.420060      1.888424     -0.216660       -0.035429   \n",
       "\n",
       "       Sex_ Female  Sex_ Male  Wc_ Federal-gov  Wc_ Local-gov  \\\n",
       "0                0          1                0              0   \n",
       "1                0          1                0              0   \n",
       "2                0          1                0              0   \n",
       "3                0          1                0              0   \n",
       "4                1          0                0              0   \n",
       "5                1          0                0              0   \n",
       "6                1          0                0              0   \n",
       "7                0          1                0              0   \n",
       "8                1          0                0              0   \n",
       "9                0          1                0              0   \n",
       "10               0          1                0              0   \n",
       "11               0          1                0              0   \n",
       "12               1          0                0              0   \n",
       "13               0          1                0              0   \n",
       "14               0          1                0              0   \n",
       "15               0          1                0              0   \n",
       "16               0          1                0              0   \n",
       "17               0          1                0              0   \n",
       "18               0          1                0              0   \n",
       "19               1          0                0              0   \n",
       "20               0          1                0              0   \n",
       "21               1          0                0              0   \n",
       "22               0          1                1              0   \n",
       "23               0          1                0              0   \n",
       "24               1          0                0              0   \n",
       "25               0          1                0              1   \n",
       "26               0          1                0              0   \n",
       "27               0          1                0              0   \n",
       "28               0          1                0              0   \n",
       "29               0          1                0              0   \n",
       "...            ...        ...              ...            ...   \n",
       "32531            1          0                0              0   \n",
       "32532            0          1                0              0   \n",
       "32533            0          1                0              0   \n",
       "32534            1          0                0              0   \n",
       "32535            0          1                0              0   \n",
       "32536            1          0                0              0   \n",
       "32537            0          1                0              0   \n",
       "32538            1          0                0              0   \n",
       "32539            0          1                0              0   \n",
       "32540            1          0                0              0   \n",
       "32541            1          0                0              0   \n",
       "32542            0          1                0              0   \n",
       "32543            1          0                0              1   \n",
       "32544            1          0                0              0   \n",
       "32545            1          0                0              1   \n",
       "32546            1          0                0              0   \n",
       "32547            0          1                0              0   \n",
       "32548            0          1                0              0   \n",
       "32549            1          0                0              0   \n",
       "32550            0          1                0              0   \n",
       "32551            0          1                0              0   \n",
       "32552            0          1                0              0   \n",
       "32553            0          1                0              0   \n",
       "32554            0          1                0              0   \n",
       "32555            0          1                0              0   \n",
       "32556            1          0                0              0   \n",
       "32557            0          1                0              0   \n",
       "32558            1          0                0              0   \n",
       "32559            0          1                0              0   \n",
       "32560            1          0                0              0   \n",
       "\n",
       "       Wc_ Never-worked        ...          Cou_ Portugal  Cou_ Puerto-Rico  \\\n",
       "0                     0        ...                      0                 0   \n",
       "1                     0        ...                      0                 0   \n",
       "2                     0        ...                      0                 0   \n",
       "3                     0        ...                      0                 0   \n",
       "4                     0        ...                      0                 0   \n",
       "5                     0        ...                      0                 0   \n",
       "6                     0        ...                      0                 0   \n",
       "7                     0        ...                      0                 0   \n",
       "8                     0        ...                      0                 0   \n",
       "9                     0        ...                      0                 0   \n",
       "10                    0        ...                      0                 0   \n",
       "11                    0        ...                      0                 0   \n",
       "12                    0        ...                      0                 0   \n",
       "13                    0        ...                      0                 0   \n",
       "14                    0        ...                      0                 0   \n",
       "15                    0        ...                      0                 0   \n",
       "16                    0        ...                      0                 0   \n",
       "17                    0        ...                      0                 0   \n",
       "18                    0        ...                      0                 0   \n",
       "19                    0        ...                      0                 0   \n",
       "20                    0        ...                      0                 0   \n",
       "21                    0        ...                      0                 0   \n",
       "22                    0        ...                      0                 0   \n",
       "23                    0        ...                      0                 0   \n",
       "24                    0        ...                      0                 0   \n",
       "25                    0        ...                      0                 0   \n",
       "26                    0        ...                      0                 0   \n",
       "27                    0        ...                      0                 0   \n",
       "28                    0        ...                      0                 0   \n",
       "29                    0        ...                      0                 0   \n",
       "...                 ...        ...                    ...               ...   \n",
       "32531                 0        ...                      0                 0   \n",
       "32532                 0        ...                      0                 0   \n",
       "32533                 0        ...                      0                 0   \n",
       "32534                 0        ...                      0                 0   \n",
       "32535                 0        ...                      0                 0   \n",
       "32536                 0        ...                      0                 0   \n",
       "32537                 0        ...                      0                 0   \n",
       "32538                 0        ...                      0                 0   \n",
       "32539                 0        ...                      0                 0   \n",
       "32540                 0        ...                      0                 0   \n",
       "32541                 0        ...                      0                 0   \n",
       "32542                 0        ...                      0                 0   \n",
       "32543                 0        ...                      0                 0   \n",
       "32544                 0        ...                      0                 0   \n",
       "32545                 0        ...                      0                 0   \n",
       "32546                 0        ...                      0                 0   \n",
       "32547                 0        ...                      0                 0   \n",
       "32548                 0        ...                      0                 0   \n",
       "32549                 0        ...                      0                 0   \n",
       "32550                 0        ...                      0                 0   \n",
       "32551                 0        ...                      0                 0   \n",
       "32552                 0        ...                      0                 0   \n",
       "32553                 0        ...                      0                 0   \n",
       "32554                 0        ...                      0                 0   \n",
       "32555                 0        ...                      0                 0   \n",
       "32556                 0        ...                      0                 0   \n",
       "32557                 0        ...                      0                 0   \n",
       "32558                 0        ...                      0                 0   \n",
       "32559                 0        ...                      0                 0   \n",
       "32560                 0        ...                      0                 0   \n",
       "\n",
       "       Cou_ Scotland  Cou_ South  Cou_ Taiwan  Cou_ Thailand  \\\n",
       "0                  0           0            0              0   \n",
       "1                  0           0            0              0   \n",
       "2                  0           0            0              0   \n",
       "3                  0           0            0              0   \n",
       "4                  0           0            0              0   \n",
       "5                  0           0            0              0   \n",
       "6                  0           0            0              0   \n",
       "7                  0           0            0              0   \n",
       "8                  0           0            0              0   \n",
       "9                  0           0            0              0   \n",
       "10                 0           0            0              0   \n",
       "11                 0           0            0              0   \n",
       "12                 0           0            0              0   \n",
       "13                 0           0            0              0   \n",
       "14                 0           0            0              0   \n",
       "15                 0           0            0              0   \n",
       "16                 0           0            0              0   \n",
       "17                 0           0            0              0   \n",
       "18                 0           0            0              0   \n",
       "19                 0           0            0              0   \n",
       "20                 0           0            0              0   \n",
       "21                 0           0            0              0   \n",
       "22                 0           0            0              0   \n",
       "23                 0           0            0              0   \n",
       "24                 0           0            0              0   \n",
       "25                 0           0            0              0   \n",
       "26                 0           0            0              0   \n",
       "27                 0           1            0              0   \n",
       "28                 0           0            0              0   \n",
       "29                 0           0            0              0   \n",
       "...              ...         ...          ...            ...   \n",
       "32531              0           0            0              0   \n",
       "32532              0           0            0              0   \n",
       "32533              0           0            0              0   \n",
       "32534              0           0            0              0   \n",
       "32535              0           0            0              0   \n",
       "32536              0           0            0              0   \n",
       "32537              0           0            0              0   \n",
       "32538              0           0            0              0   \n",
       "32539              0           0            0              0   \n",
       "32540              0           0            0              0   \n",
       "32541              0           0            0              0   \n",
       "32542              0           0            0              0   \n",
       "32543              0           0            0              0   \n",
       "32544              0           0            0              0   \n",
       "32545              0           0            0              0   \n",
       "32546              0           0            0              0   \n",
       "32547              0           0            0              0   \n",
       "32548              0           0            0              0   \n",
       "32549              0           0            0              0   \n",
       "32550              0           0            0              0   \n",
       "32551              0           0            0              0   \n",
       "32552              0           0            0              0   \n",
       "32553              0           0            1              0   \n",
       "32554              0           0            0              0   \n",
       "32555              0           0            0              0   \n",
       "32556              0           0            0              0   \n",
       "32557              0           0            0              0   \n",
       "32558              0           0            0              0   \n",
       "32559              0           0            0              0   \n",
       "32560              0           0            0              0   \n",
       "\n",
       "       Cou_ Trinadad&Tobago  Cou_ Vietnam  Cou_ Yugoslavia  Cou_United-States  \n",
       "0                         0             0                0                  1  \n",
       "1                         0             0                0                  1  \n",
       "2                         0             0                0                  1  \n",
       "3                         0             0                0                  1  \n",
       "4                         0             0                0                  0  \n",
       "5                         0             0                0                  1  \n",
       "6                         0             0                0                  0  \n",
       "7                         0             0                0                  1  \n",
       "8                         0             0                0                  1  \n",
       "9                         0             0                0                  1  \n",
       "10                        0             0                0                  1  \n",
       "11                        0             0                0                  0  \n",
       "12                        0             0                0                  1  \n",
       "13                        0             0                0                  1  \n",
       "14                        0             0                0                  1  \n",
       "15                        0             0                0                  0  \n",
       "16                        0             0                0                  1  \n",
       "17                        0             0                0                  1  \n",
       "18                        0             0                0                  1  \n",
       "19                        0             0                0                  1  \n",
       "20                        0             0                0                  1  \n",
       "21                        0             0                0                  1  \n",
       "22                        0             0                0                  1  \n",
       "23                        0             0                0                  1  \n",
       "24                        0             0                0                  1  \n",
       "25                        0             0                0                  1  \n",
       "26                        0             0                0                  1  \n",
       "27                        0             0                0                  0  \n",
       "28                        0             0                0                  1  \n",
       "29                        0             0                0                  1  \n",
       "...                     ...           ...              ...                ...  \n",
       "32531                     0             0                0                  1  \n",
       "32532                     0             0                0                  1  \n",
       "32533                     0             0                0                  0  \n",
       "32534                     0             0                0                  1  \n",
       "32535                     0             0                0                  1  \n",
       "32536                     0             0                0                  1  \n",
       "32537                     0             0                0                  1  \n",
       "32538                     0             0                0                  1  \n",
       "32539                     0             0                0                  1  \n",
       "32540                     0             0                0                  1  \n",
       "32541                     0             0                0                  1  \n",
       "32542                     0             0                0                  1  \n",
       "32543                     0             0                0                  1  \n",
       "32544                     0             0                0                  1  \n",
       "32545                     0             0                0                  1  \n",
       "32546                     0             0                0                  1  \n",
       "32547                     0             0                0                  0  \n",
       "32548                     0             0                0                  1  \n",
       "32549                     0             0                0                  1  \n",
       "32550                     0             0                0                  1  \n",
       "32551                     0             0                0                  1  \n",
       "32552                     0             0                0                  1  \n",
       "32553                     0             0                0                  0  \n",
       "32554                     0             0                0                  1  \n",
       "32555                     0             0                0                  1  \n",
       "32556                     0             0                0                  1  \n",
       "32557                     0             0                0                  1  \n",
       "32558                     0             0                0                  1  \n",
       "32559                     0             0                0                  1  \n",
       "32560                     0             0                0                  1  \n",
       "\n",
       "[32561 rows x 88 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对Capital_Gain和Capital_Loss两个属性的值进行标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "dummy_train_data_df['Capital_Gain'] = scaler.fit(dummy_train_data_df['Capital_Gain'].values.reshape(-1,1)).transform(dummy_train_data_df['Capital_Gain'].values.reshape(-1,1))\n",
    "dummy_train_data_df['Capital_Loss'] = scaler.fit(dummy_train_data_df['Capital_Loss'].values.reshape(-1,1)).transform(dummy_train_data_df['Capital_Loss'].values.reshape(-1,1))\n",
    "\n",
    "dummy_train_data_df['Age'] = scaler.fit(dummy_train_data_df['Age'].values.reshape(-1,1)).transform(dummy_train_data_df['Age'].values.reshape(-1,1))\n",
    "dummy_train_data_df['Education_Num'] = scaler.fit(dummy_train_data_df['Education_Num'].values.reshape(-1,1)).transform(dummy_train_data_df['Education_Num'].values.reshape(-1,1))\n",
    "dummy_train_data_df['Hours_per_week'] = scaler.fit(dummy_train_data_df['Hours_per_week'].values.reshape(-1,1)).transform(dummy_train_data_df['Hours_per_week'].values.reshape(-1,1))\n",
    "\n",
    "dummy_test_data_df['Capital_Gain'] = scaler.fit(dummy_test_data_df['Capital_Gain'].values.reshape(-1,1)).transform(dummy_test_data_df['Capital_Gain'].values.reshape(-1,1))\n",
    "dummy_test_data_df['Capital_Loss'] = scaler.fit(dummy_test_data_df['Capital_Loss'].values.reshape(-1,1)).transform(dummy_test_data_df['Capital_Loss'].values.reshape(-1,1))\n",
    "\n",
    "dummy_test_data_df['Age'] = scaler.fit(dummy_test_data_df['Age'].values.reshape(-1,1)).transform(dummy_test_data_df['Age'].values.reshape(-1,1))\n",
    "dummy_test_data_df['Education_Num'] = scaler.fit(dummy_test_data_df['Education_Num'].values.reshape(-1,1)).transform(dummy_test_data_df['Education_Num'].values.reshape(-1,1))\n",
    "dummy_test_data_df['Hours_per_week'] = scaler.fit(dummy_test_data_df['Hours_per_week'].values.reshape(-1,1)).transform(dummy_test_data_df['Hours_per_week'].values.reshape(-1,1))\n",
    "\n",
    "dummy_train_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#取出值\n",
    "train_df = dummy_train_data_df.values\n",
    "train_target = dummy_train_data_target.values\n",
    "\n",
    "test_df = dummy_test_data_df.values\n",
    "test_target = dummy_test_data_target.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = [LogisticRegression(),DecisionTreeClassifier(),RandomForestClassifier(),GradientBoostingClassifier(),SVC()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lr:0.8511103383857875,[0.84738216 0.84920147 0.85273342 0.85365479 0.85257985]\n",
      "Tree:0.8179417106063813,[0.81790266 0.81787469 0.81219287 0.8215602  0.82017813]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF:0.8431251558497067,[0.84492553 0.83845209 0.8379914  0.8458231  0.84843366]\n",
      "GDBT:0.8651148185579324,[0.86150775 0.86194717 0.8671683  0.86732187 0.86762899]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM:0.853843706463467,[0.84891755 0.85273342 0.85703317 0.85780098 0.85273342]\n"
     ]
    }
   ],
   "source": [
    "names = ['Lr','Tree','RF','GDBT','SVM']\n",
    "for name,model in zip(names,model):\n",
    "    score = cross_val_score(model,train_df,train_target,cv=5)\n",
    "    print(\"{}:{},{}\".format(name,score.mean(),score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.22487713e-02, 2.05717479e-01, 2.01020580e-01, 5.93382962e-02,\n",
       "       3.63689487e-02, 9.56294064e-04, 1.70980719e-03, 1.62995389e-03,\n",
       "       1.30787316e-03, 0.00000000e+00, 9.45272647e-04, 3.49863972e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 2.35263226e-04, 0.00000000e+00,\n",
       "       6.62466067e-04, 3.82736203e-01, 0.00000000e+00, 3.92427281e-04,\n",
       "       1.47426362e-04, 8.96794115e-05, 1.99386904e-04, 0.00000000e+00,\n",
       "       2.96881029e-05, 1.62813708e-02, 5.26064215e-03, 6.14493764e-04,\n",
       "       3.78026334e-04, 4.95942989e-03, 0.00000000e+00, 6.78163482e-04,\n",
       "       5.90322212e-04, 1.51781799e-03, 7.87659500e-05, 1.12882580e-03,\n",
       "       1.41198519e-03, 6.48877066e-04, 0.00000000e+00, 5.38898041e-04,\n",
       "       2.35222379e-04, 4.71364270e-03, 1.47221829e-04, 0.00000000e+00,\n",
       "       7.51927212e-05, 0.00000000e+00, 4.79267391e-04, 9.19221258e-05,\n",
       "       5.47303353e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.52014544e-05,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.20248064e-04,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 4.27290452e-05, 0.00000000e+00,\n",
       "       5.27993933e-05, 0.00000000e+00, 1.49641318e-04, 0.00000000e+00,\n",
       "       9.25844569e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.25035477e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.23701062e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.87846725e-05])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GradientBoostingClassifier()\n",
    "model.fit(train_df,train_target)\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MS_ Married-civ-spouse</th>\n",
       "      <td>0.382736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education_Num</th>\n",
       "      <td>0.205717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital_Gain</th>\n",
       "      <td>0.201021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.062249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital_Loss</th>\n",
       "      <td>0.059338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hours_per_week</th>\n",
       "      <td>0.036369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Exec-managerial</th>\n",
       "      <td>0.016281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Farming-fishing</th>\n",
       "      <td>0.005261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Other-service</th>\n",
       "      <td>0.004959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Wife</th>\n",
       "      <td>0.004714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_ Self-emp-not-inc</th>\n",
       "      <td>0.003499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_ Male</th>\n",
       "      <td>0.001710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_ Federal-gov</th>\n",
       "      <td>0.001630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Tech-support</th>\n",
       "      <td>0.001518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Husband</th>\n",
       "      <td>0.001412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_ Local-gov</th>\n",
       "      <td>0.001308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_Prof-specialty</th>\n",
       "      <td>0.001129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_ Female</th>\n",
       "      <td>0.000956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_ Self-emp-inc</th>\n",
       "      <td>0.000945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Protective-serv</th>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ Married-AF-spouse</th>\n",
       "      <td>0.000662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Not-in-family</th>\n",
       "      <td>0.000649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Handlers-cleaners</th>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Sales</th>\n",
       "      <td>0.000590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Own-child</th>\n",
       "      <td>0.000539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race_ White</th>\n",
       "      <td>0.000479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ Never-married</th>\n",
       "      <td>0.000392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Machine-op-inspct</th>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_Private</th>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Unmarried</th>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Poland</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Portugal</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Ireland</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Scotland</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wc_ Never-worked</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Taiwan</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Thailand</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Trinadad&amp;Tobago</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Puerto-Rico</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rel_ Other-relative</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race_ Asian-Pac-Islander</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Hungary</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race_ Other</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Priv-house-serv</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ China</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Columbia</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Cuba</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Dominican-Republic</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Ecuador</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ El-Salvador</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occ_ Armed-Forces</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ France</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Germany</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Greece</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ Married-spouse-absent</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Haiti</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Holand-Netherlands</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Honduras</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ Hong</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cou_ India</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           importance\n",
       "MS_ Married-civ-spouse       0.382736\n",
       "Education_Num                0.205717\n",
       "Capital_Gain                 0.201021\n",
       "Age                          0.062249\n",
       "Capital_Loss                 0.059338\n",
       "Hours_per_week               0.036369\n",
       "Occ_ Exec-managerial         0.016281\n",
       "Occ_ Farming-fishing         0.005261\n",
       "Occ_ Other-service           0.004959\n",
       "Rel_ Wife                    0.004714\n",
       "Wc_ Self-emp-not-inc         0.003499\n",
       "Sex_ Male                    0.001710\n",
       "Wc_ Federal-gov              0.001630\n",
       "Occ_ Tech-support            0.001518\n",
       "Rel_ Husband                 0.001412\n",
       "Wc_ Local-gov                0.001308\n",
       "Occ_Prof-specialty           0.001129\n",
       "Sex_ Female                  0.000956\n",
       "Wc_ Self-emp-inc             0.000945\n",
       "Occ_ Protective-serv         0.000678\n",
       "MS_ Married-AF-spouse        0.000662\n",
       "Rel_ Not-in-family           0.000649\n",
       "Occ_ Handlers-cleaners       0.000614\n",
       "Occ_ Sales                   0.000590\n",
       "Rel_ Own-child               0.000539\n",
       "Race_ White                  0.000479\n",
       "MS_ Never-married            0.000392\n",
       "Occ_ Machine-op-inspct       0.000378\n",
       "Wc_Private                   0.000235\n",
       "Rel_ Unmarried               0.000235\n",
       "...                               ...\n",
       "Cou_ Poland                  0.000000\n",
       "Cou_ Portugal                0.000000\n",
       "Cou_ Ireland                 0.000000\n",
       "Cou_ Scotland                0.000000\n",
       "Wc_ Never-worked             0.000000\n",
       "Cou_ Taiwan                  0.000000\n",
       "Cou_ Thailand                0.000000\n",
       "Cou_ Trinadad&Tobago         0.000000\n",
       "Cou_ Puerto-Rico             0.000000\n",
       "Rel_ Other-relative          0.000000\n",
       "Race_ Asian-Pac-Islander     0.000000\n",
       "Cou_ Hungary                 0.000000\n",
       "Race_ Other                  0.000000\n",
       "Occ_ Priv-house-serv         0.000000\n",
       "Cou_ China                   0.000000\n",
       "Cou_ Columbia                0.000000\n",
       "Cou_ Cuba                    0.000000\n",
       "Cou_ Dominican-Republic      0.000000\n",
       "Cou_ Ecuador                 0.000000\n",
       "Cou_ El-Salvador             0.000000\n",
       "Occ_ Armed-Forces            0.000000\n",
       "Cou_ France                  0.000000\n",
       "Cou_ Germany                 0.000000\n",
       "Cou_ Greece                  0.000000\n",
       "MS_ Married-spouse-absent    0.000000\n",
       "Cou_ Haiti                   0.000000\n",
       "Cou_ Holand-Netherlands      0.000000\n",
       "Cou_ Honduras                0.000000\n",
       "Cou_ Hong                    0.000000\n",
       "Cou_ India                   0.000000\n",
       "\n",
       "[88 rows x 1 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = pd.DataFrame({'importance':model.feature_importances_},index=dummy_train_data_df.columns)\n",
    "fi.sort_values('importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature Importance')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi.sort_values('importance',ascending=False).plot.bar(figsize=(11,7))\n",
    "plt.xticks(rotation=30)\n",
    "plt.title('Feature Importance',size='x-large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#超参数调整\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'C': 1}, 0.8511102238874727)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#逻辑回归\n",
    "param_grid = {'C':[0.01,0.1,1,10]}\n",
    "grid_search = GridSearchCV(LogisticRegression(),param_grid,cv=5)\n",
    "grid_search.fit(train_df,train_target)\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'C': 0.5}, 0.8512944934123645)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second round grid search\n",
    "param_grid={'C':[0.5,0.8,1,1.2,1.5]}\n",
    "grid_search=GridSearchCV(LogisticRegression(),param_grid,cv=5)\n",
    "\n",
    "grid_search.fit(train_df,train_target)\n",
    "\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'C': 1, 'gamma': 0.1}, 0.8574982340837198)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#支持向量机\n",
    "param_grid={'C':[0.01,0.1,1,10],'gamma':[0.01,0.1,1,10]}\n",
    "grid_search=GridSearchCV(SVC(),param_grid,cv=5)\n",
    "\n",
    "grid_search.fit(train_df,train_target)\n",
    "\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200},\n",
       " 0.8734375479868555)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting Decision Tree\n",
    "param_grid ={'n_estimators':[30,50,80,120,200],'learning_rate':[0.05,0.1,0.5,1],'max_depth':[1,2,3,4,5]}\n",
    "grid_search=GridSearchCV(GradientBoostingClassifier(),param_grid,cv=5)\n",
    "\n",
    "grid_search.fit(train_df,train_target)\n",
    "\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200},\n",
       " 0.8735911059242653)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second round grid search\n",
    "param_grid ={'n_estimators':[120,200,250,300],'learning_rate':[0.05,0.1,0.5,1],'max_depth':[4,5,8,11]}\n",
    "grid_search=GridSearchCV(GradientBoostingClassifier(),param_grid,cv=5)\n",
    "\n",
    "grid_search.fit(train_df,train_target)\n",
    "\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=150, n_jobs=None, oob_score=False,\n",
       "         random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "#使用逻辑回归作为基分类器\n",
    "bagging = BaggingClassifier(LogisticRegression(C=0.5),n_estimators=150)\n",
    "bagging.fit(train_df,train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8527117498925127"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=bagging.predict(test_df)\n",
    "count=0\n",
    "for res,tar in zip(result,test_target):\n",
    "    if res==tar:\n",
    "        count=count+1\n",
    "accuracy = count/len(result)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8527117498925127"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging.score(test_df,test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
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     ]
    },
    {
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      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
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      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "          learning_rate=1.0, n_estimators=150, random_state=None)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada = AdaBoostClassifier(base_estimator=LogisticRegression(C=0.5),n_estimators=150)\n",
    "ada.fit(train_df,train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.847798046803022"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(test_df,test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=5,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用GradientBoostingClassifier\n",
    "gbt = GradientBoostingClassifier(n_estimators=200,learning_rate=0.1,max_depth=5)\n",
    "gbt.fit(train_df,train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8656716417910447"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbt.score(test_df,test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 9, 'n_estimators': 300}, 0.8594023525076011)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用随机森林  也是Bagging的一种\n",
    "param_grid ={'n_estimators':[100,200,300,400],'max_depth':[3,5,7,9]}\n",
    "grid_search=GridSearchCV(RandomForestClassifier(),param_grid,cv=5)\n",
    "\n",
    "grid_search.fit(train_df,train_target)\n",
    "\n",
    "grid_search.best_params_,grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8588538787543762"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=300,max_depth=9)\n",
    "rf.fit(train_df,train_target)\n",
    "rf.score(test_df,test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对stacking算法\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "n_train=train_df.shape[0]\n",
    "n_test=test_df.shape[0]\n",
    "kf=StratifiedKFold(n_splits=5,random_state=1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def get_oof(clf,X,y,test_X):\n",
    "    oof_train=np.zeros((n_train,))\n",
    "    oof_test_mean=np.zeros((n_test,))\n",
    "    oof_test_single=np.empty((5,n_test))\n",
    "    for i, (train_index,val_index) in enumerate(kf.split(X,y)):\n",
    "        kf_X_train=X[train_index]\n",
    "        kf_y_train=y[train_index]\n",
    "        kf_X_val=X[val_index]\n",
    "        \n",
    "        clf.fit(kf_X_train,kf_y_train)\n",
    "        \n",
    "        oof_train[val_index]=clf.predict(kf_X_val)\n",
    "        oof_test_single[i,:]=clf.predict(test_X)\n",
    "    oof_test_mean=oof_test_single.mean(axis=0)\n",
    "    return oof_train.reshape(-1,1), oof_test_mean.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "LR_train,LR_test=get_oof(LogisticRegression(C=0.5),train_df,train_target,test_df)\n",
    "SVM_train,SVM_test=get_oof(SVC(gamma=0.1),train_df,train_target,test_df)\n",
    "GBDT_train,GBDT_test=get_oof(GradientBoostingClassifier(n_estimators=200,learning_rate=0.1,max_depth=5),train_df,train_target,test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_stack=np.concatenate((LR_train,SVM_train,GBDT_train),axis=1)\n",
    "y_stack=train_target\n",
    "X_test_stack=np.concatenate((LR_test,SVM_test,GBDT_test),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8725470435051272,\n",
       " array([0.86826347, 0.87039312, 0.87361794, 0.8762285 , 0.87423219]))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_score=cross_val_score(RandomForestClassifier(n_estimators=500),X_stack,y_stack,cv=5)\n",
    "stack_score.mean(),stack_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(n_estimators=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_stack,y_stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8655487992138075"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test_stack,test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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